Journal of Medical and Health Studies (ISSN: 2710-1452) is a peer-reviewed International Journal published by Al-Kindi Center for Research and Development. The journal covers scholarly articles in the fields of Medicine and Public Health. All articles are freely available without charge to the users or their institutions; thus, ensuring high visibility and increasing citations for all research articles published. The journal aims to facilitate scholarly work on recent theoretical and practical aspects of health and medical sciences. Academics, policymakers, and researchers are welcome to submit their manuscripts at any time.
Al-Kindi Center for Research and Development
Body-Contouring Surgery After GLP-1–Induced Massive Weight Loss: Surgical Outcomes, Complications, and Patient-Reported Quality of Life
The rapid adoption of glucagon-like peptide-1 receptor agonists (GLP-1 RAs) has created a growing population presenting for body-contouring surgery after pharmacologically induced massive weight loss (MWL). Whether outcomes differ from those after bariatric-induced MWL is uncertain. This study compared surgical outcomes, 90-day complications, and patient-reported quality of life between the two groups. In this prospective comparative cohort, 90 adults undergoing body contouring after MWL were grouped by weight-loss modality: GLP-1 RA pharmacotherapy (n = 45) or bariatric surgery (n = 45). Procedures included abdominoplasty/panniculectomy with or without brachioplasty, thighplasty, or mastopexy. The primary outcome was the incidence of complications within 90 days; the secondary outcome was change in the BODY-Q (Satisfaction with Body, Psychological and Physical Function) from baseline to 6 months. Predictors of complications were examined by multivariable logistic regression. The groups were similar in age and sex; the bariatric group had a higher peak body mass index (BMI) and greater total weight loss. The overall 90-day complication rate did not differ between GLP-1 and bariatric groups (19.0% vs 22.2%; p = 0.69), nor did any individual complication. BODY-Q scores improved markedly in both groups (Satisfaction with Body +33.3 vs +36.3 points; both p < 0.001), with no significant between-group difference. On multivariable analysis, BMI at surgery (adjusted OR 1.78 per 5 kg/m²; p = 0.02) and current smoking (OR 2.40; p = 0.04) predicted complications, whereas weight-loss modality did not (OR 0.83; p = 0.69). Body contouring after GLP-1–induced MWL produced complication rates and quality-of-life gains comparable to those after bariatric-induced MWL. Residual obesity and smoking, rather than the method of weight loss, drove complication risk. These findings support offering body contouring to GLP-1 patients with attention to perioperative optimisation.
GLP-1 receptor agonists; massive weight loss; body contouring; abdominoplasty; complications; BODY-Q; patient-reported outcomes
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Early versus Delayed Excision and Grafting in Deep Burns: Impact on Healing Time and Hospital Stay
Excision and grafting is the standard surgical treatment of deep burns, but the optimal timing remains debated. This study compared early versus delayed excision and grafting (E&G) with respect to wound healing time and length of hospital stay. In this prospective comparative cohort, 80 adults with deep partial- and full-thickness burns of 10–30% total body surface area (TBSA) were managed by early E&G (excision within 5 days of injury; n = 40) or delayed E&G (excision after eschar separation, beyond 14 days; n = 40). The primary outcomes were time to complete wound healing and length of hospital stay. Secondary outcomes were graft take, wound infection, number of operations, blood-transfusion requirement, and mortality. Groups were compared with independent-samples t-tests and chi-square tests, and length of stay was modelled by multivariable linear regression. The two groups were comparable at baseline (mean age 35.5 years; mean TBSA 18.4%). Early E&G was associated with shorter time to complete healing (24.3 ± 6.1 vs 38.7 ± 8.9 days; mean difference 14.4 days; 95% CI 11.0–17.8; p < 0.001) and shorter hospital stay (19.8 ± 5.7 vs 31.4 ± 8.2 days; mean difference 11.6 days; 95% CI 8.5–14.7; p < 0.001). Graft take was higher (92.4% vs 88.1%; p = 0.03) and wound infection lower (17.5% vs 37.5%; p = 0.045) in the early group. Early E&G required more blood transfusion (3.1 ± 1.4 vs 1.9 ± 1.1 units; p < 0.001). Mortality did not differ (5.0% vs 7.5%; p = 0.64). Early excision and grafting of deep burns shortened healing time and hospital stay and reduced wound infection, at the cost of greater intraoperative blood transfusion. These findings support early surgery for suitable patients with deep burns, with attention to perioperative blood management.
deep burns; early excision; skin grafting; wound healing; length of stay; surgical timing
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Converging Wearable Biosensors, Multi-Omics, and Artificial Intelligence: An Integrative Framework for Predictive and Preventive Precision Medicine
Precision medicine aspires to tailor prevention, diagnosis, and treatment to the individual rather than to a statistical average, yet that ambition has long outrun the data and the tools needed to deliver it. Three streams of biomedical information now make the goal tangible, even if they have matured along largely separate paths: high-dimensional molecular profiles drawn from multi-omics platforms, continuously recorded physiological signals from wearable biosensors, and the messy, accumulating record of routine clinical care. Artificial intelligence, and machine learning in particular, has become the connective tissue that can bind these unlike sources into models a clinician might actually use. This article advances an integrative framework in which multi-omics data establish a patient's molecular baseline, wearable sensors trace the longitudinal phenotype as it shifts from day to day, and learning algorithms translate the combined signal into predictions that are both clinically meaningful and individually calibrated. We review the methodological terrain of data integration, trace applications across oncology, cardiovascular medicine, chronic disease prevention, and drug discovery, and weigh the obstacles, spanning data heterogeneity, model opacity, validation, equity, and privacy, that still separate promising prototypes from everyday practice. Our central argument is that the most durable near-term gains will not come from any single modality in isolation but from disciplined, transparent fusion of all three, anchored by rigorous external validation and a steady concern for fairness.
Precision Medicine; Multi-Omics; Machine Learning; Deep Learning; Wearable Biosensors; Data Integration; Predictive Analytics
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Optimizing Red Blood Cell Transfusion Thresholds Across Medical, Surgical, and Obstetric Populations: A Systematic Review
Red blood cell (RBC) transfusion remains a cornerstone of supportive care across medical, surgical, and obstetric settings; however, the optimal hemoglobin threshold for transfusion continues to be debated due to concerns regarding transfusion-related complications, resource utilization, and patient outcomes. This systematic review aimed to evaluate the efficacy and safety of restrictive versus liberal RBC transfusion strategies across diverse adult patient populations. A comprehensive literature search of PubMed/MEDLINE, Embase, Scopus, Web of Science, Cochrane Library, and ClinicalTrials.gov was conducted from database inception through June 2026 in accordance with PRISMA 2020 guidelines. Studies comparing restrictive transfusion thresholds (typically 7–8 g/dL) with liberal thresholds (typically 9–10 g/dL) were included. A total of 97 studies involving more than 350,000 patients were analyzed. Across critically ill patients, septic shock, gastrointestinal bleeding, orthopedic surgery, and cardiac surgery populations, restrictive transfusion strategies consistently reduced blood product utilization without increasing mortality, cardiovascular complications, infection rates, length of hospital stay, or other major adverse outcomes. In patients with acute gastrointestinal bleeding, restrictive transfusion was associated with improved survival and lower rebleeding rates. Evidence from obstetric populations suggested similar maternal outcomes with reduced transfusion exposure, although data were less robust than those available for medical and surgical cohorts. Uncertainty persists in selected high-risk populations, including patients with acute coronary syndromes, severe cardiovascular disease, and major obstetric hemorrhage, where individualized clinical assessment remains essential. Overall, current evidence strongly supports restrictive RBC transfusion strategies as a safe, effective, and resource-efficient approach for most hemodynamically stable adult patients, reinforcing contemporary patient blood management principles and the need to minimize unnecessary transfusion exposure while maintaining optimal clinical outcomes.
Red blood cell transfusion; Restrictive transfusion; Liberal transfusion; Hemoglobin threshold; Patient blood management; Critical care; Surgery; Obstetrics; Anemia; Systematic review.
Copyright (c) 2026 Journal of Medical and Health Studies
Multi-Scale Attention-Fusion Classification with an LLM-Driven Clinical Recommendation Assistant for Brain Tumor MRI
Brain tumor MRI interpretation remains constrained by radiologist shortages, inter-reader variability, delayed specialist access, and high diagnostic cost, particularly in low-resource settings. Although deep learning classifiers can assign tumor labels, a class prediction alone does not provide actionable guidance on urgency, referral, follow-up, or patient-facing interpretation. This study proposed a combined diagnostic and recommendation framework comprising an MSAF-Net classifier and a confidence-conditioned retrieval-augmented GPT-2 clinical assistant. MSAF-Net used a ConvNeXt-T/Swin-T hierarchical stem, per-stage channel-spatial attention, gated cross-scale feature fusion, and a compact classification head for four-class brain tumor MRI classification. The assistant converted the predicted class and confidence score into a structured query, retrieved relevant evidence from a curated brain-tumor knowledge base, and generated a plain-language, evidence-grounded recommendation with a PDF report. On the 7,023-image four-class Kaggle brain tumor MRI dataset, MSAF-Net achieved 99.16% accuracy, 0.9897 macro-F1, and 0.9985 AUC. Cross-dataset evaluation showed 99.46% accuracy on Figshare CE-MRI and 99.74% accuracy on BR35H. The model remained computationally efficient, with 22.9M parameters, 3.54 GFLOPs, 10 ms inference per scan, and an expected calibration error of 0.0179. These findings indicate that the proposed framework can support accurate classification while translating predictions into clinically usable next-step recommendations. The system is intended to assist, not replace, clinicians by improving decision support, reducing per-case cost, and widening access to neuroimaging guidance.
Brain tumor MRI; MSAF-Net; attention fusion; GPT-2; retrieval-augmented generation; clinical decision support
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Incidence and Risk Factors of Third- and Fourth-Degree Perineal Tears in Vaginal Delivery: A Retrospective Study at Bint Al-Huda Teaching Hospital, Nasiriyah, Iraq
Obstetric anal sphincter injuries (OASIS) — third- and fourth-degree perineal tears — are an important cause of maternal morbidity, including anal incontinence, perineal pain, dyspareunia, and psychological distress. Their reported incidence varies widely (0.6–19.3%) with detection practices and case-mix. This study determined the incidence of third- and fourth-degree tears among vaginal deliveries and identified independent risk factors at a tertiary maternity hospital in southern Iraq. A retrospective hospital-based study (cross-sectional for incidence with a nested case–control component for risk factors) was conducted at Bint Al-Huda Teaching Hospital, Nasiriyah, over the period 2023–2025. All vaginal deliveries were classified for perineal trauma using the RCOG/Sultan classification. Cases (OASIS) were compared with controls (vaginal delivery without OASIS). Maternal, intrapartum, and fetal variables were analysed by univariate testing followed by multivariable logistic regression, reporting adjusted odds ratios (aOR) with 95% confidence intervals; p<0.05 was significant. Reporting followed STROBE. Among 3,000 vaginal deliveries, OASIS occurred in 150 (5.0%) — 126 (4.2%) third-degree and 24 (0.8%) fourth-degree. On multivariable analysis, the independent risk factors were forceps delivery (aOR 6.20), nulliparity (aOR 5.80), occipito-posterior position (aOR 2.44), birthweight >4 kg (aOR 2.30), prolonged second stage (aOR 2.00), shoulder dystocia (aOR 1.90), and oxytocin augmentation (aOR 1.70); epidural analgesia was not independently associated. OASIS rates rose steeply with operative delivery (3.0% spontaneous, 9.5% vacuum, 16.0% forceps). OASIS at this centre was driven mainly by operative vaginal delivery, nulliparity, and fetal macrosomia. Selective mediolateral episiotomy (at ~60°) in instrumental delivery, structured manual perineal protection, and routine post-delivery rectal examination are recommended to reduce incidence and improve detection.
Perineal tear; obstetric anal sphincter injury; OASIS; vaginal delivery; risk factors; episiotomy; Iraq
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Duration of Prelabor Rupture of Membranes and Early-Onset Neonatal Sepsis: A Retrospective Cohort with Risk Model
Prelabor rupture of membranes (PROM) is an established risk factor for early-onset neonatal sepsis (EONS), but the quantitative relationship between rupture-to-delivery latency and EONS in resource-constrained settings without universal group B streptococcus (GBS) screening is incompletely characterized, and contemporary data from southern Iraq are absent from the indexed literature. To estimate the incidence of EONS among infants born after PROM at a single tertiary center, to quantify the association between ROM-to-delivery latency and EONS, and to develop and internally validate a multivariable risk model usable at the point of care. A retrospective single-center cohort study was conducted at Nasiriyah Teaching Hospital from January 2020 through December 2024. Singleton live births at 34 weeks of gestation or more with documented PROM and complete ROM-timing data were included. The primary outcome was EONS within 72 hours of birth, defined as culture-proven sepsis or clinical sepsis with supportive laboratory and clinical course. The primary exposure was ROM-to-delivery latency, analyzed both categorically (< 12, 12–18, > 18 hours) and per 6-hour increment. Multivariable logistic regression identified independent predictors; discrimination was assessed by the area under the receiver operating characteristic curve (AUC) with 1,000-resample bootstrap internal validation, and reporting followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) and Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) statements. Of 1,486 PROM births screened, 968 formed the analytic cohort; EONS occurred in 138 (14.3%, 95% confidence interval [CI] 12.1–16.7%), of which 37 (26.8%) were culture-proven, predominantly Klebsiella species and GBS. ROM-to-delivery latency greater than 18 hours was independently associated with EONS (adjusted odds ratio [aOR] 3.42, 95% CI 2.11–5.54), with risk rising approximately linearly (aOR 1.27 per 6-hour increment, 95% CI 1.14–1.42). The combined model achieved AUC 0.84 (95% CI 0.80–0.88); bootstrap-corrected AUC was 0.82. EONS risk rose progressively with ROM-to-delivery latency. A six-variable model identified high-risk newborns at the point of care and may inform surveillance and prophylaxis decisions where universal GBS screening is unavailable.
Prelabor rupture of membranes; early-onset neonatal sepsis; latency; chorioamnionitis; risk model; predictors; neonatal infection
Generational Differences in Dimensions of Work Values among Nurses in Community General Hospital
Generational differences in nurses’ work values introduce complexity in forming a unified nursing practice in a multi-generational healthcare setting. This study focused on the distinct work values—intrinsic, extrinsic, social, and prestige values of Generations X, Y and Z at Community General Hospital, filling the gap in understanding of how these values manifest across diverse cultural and organizational contexts of San Pablo City, Laguna. This study used an explanatory sequential mixed-method approach. The initial quantitative phase involved 101 registered nurses, representing 21 from Generation X, 40 from Generation Y, and 40 from Generation Z. Afterward, 12 purposively selected registered nurses were gathered for the qualitative phase. Quantitative data from an adapted Work Values Scale was statistically evaluated through weighted means and Kruskal-Wallis tests. This assessment revealed that while a core commitment to clinical competence, meaningful work, and patient care was steady across the entire nursing staff, individual generational cohorts possessed entirely different secondary motivations. Nurses from Generation Z were motivated primarily by the social and intrinsic values, with a strong emphasis on achieving career advancement and receiving peer mentoring. Nurses from Generation Y were motivated primarily by the financial stability that would come from their salary and benefits. Finally, Generation X emphasized workplace prestige, professional respect, and peer mentorship. Qualitative results corroborated these differences indicating that although nurses’ establish similar ethical standards, they interpret recognition and organizational support differently based on their career stage. The results of this study concluded that the hospital institution must rely on specialized values training and targeted team-dynamics initiatives to promote and build stronger collaboration and lower turnover rates. By addressing these generational differences, this research helps protect local healthcare sustainability and advances the goals of SDG 8 to build inclusive and resilient work environments.
generational differences, nurses, work values, philippines, mixed-method
A Deadly Mimic: Type A Aortic Dissection Presenting as Inferior STEMI
Acute Type A aortic dissection is a life threatening cardiovascular emergency that may present with clinical and electrocardiographic findings resembling acute myocardial infarction, creating a significant diagnostic challenge. We report the case of a 58 year old male with a history of hypertension who presented to the emergency department with sudden onset severe central chest pain radiating to the back, associated with diaphoresis, nausea, and vomiting. On arrival, he was tachycardic and relatively hypotensive but remained alert and neurologically intact. Initial electrocardiography demonstrated hyperacute T waves in the inferior leads with reciprocal ST segment depression in the anterior leads, representing an inferior STEMI equivalent pattern. High sensitivity troponin was elevated, further supporting an initial diagnosis of acute coronary syndrome. Despite treatment and preparation for urgent coronary angiography, persistent chest pain radiating to the interscapular region, mild mediastinal widening on chest radiography, and relative hypotension prompted further evaluation. Bedside transthoracic echocardiography demonstrated a mildly dilated ascending aorta, a small pericardial effusion, and findings suspicious for an intimal flap. Subsequent computed tomography angiography confirmed an extensive Stanford Type A aortic dissection involving the ascending aorta and aortic root with extension to the origin of the right coronary artery, resulting in coronary malperfusion. The patient underwent prompt medical stabilization with intravenous beta blockade and analgesia before transfer to a tertiary cardiothoracic center, where successful emergency surgical repair was performed. His postoperative recovery was uncomplicated, and he was discharged home in stable condition. This case highlights the importance of maintaining a broad differential diagnosis in patients presenting with apparent STEMI, particularly when atypical features such as abrupt pain onset, radiation to the back, hypotension, or mediastinal widening are present. Early recognition of aortic dissection and timely use of bedside imaging can prevent inappropriate antithrombotic therapy and facilitate life saving surgical intervention.
Myocardial infarction, ST elevation, Aortic dissection, Chest Pain.
Earwax (cerumen) is a natural secretion essential for ear health, but its impaction can lead to symptoms such as hearing loss, pain, and discomfort. This review article examines various earwax removal agents, classified into oil-based (e.g., olive oil, mineral oil, almond oil), water-based (e.g., sodium bicarbonate, saline, docusate sodium), and non-water/non-oil solutions (e.g., carbamide peroxide, glycerol, triethanolamine polypeptide). The review synthesizes findings from clinical studies, including a 2018 Cochrane Library review, which indicated that while active therapies improve complete earwax clearance (22%) compared to no treatment (5%), high-quality evidence supporting ear drops or sprays remains limited. Specific agents such as Cerumol®, Sofenz®, Otex®, and hydrogen peroxide are discussed in terms of their components, mechanisms of action (e.g., wax softening, gas bubble formation, hydration), efficacy, and adverse effects (e.g., irritation, pain). The article also highlights guideline recommendations from the British National Formulary (BNF) and the American Academy of Otolaryngology. It concludes that while many cerumenolytics are available, evidence varies, and patient-specific factors (e.g., eardrum perforation, age, infection) must guide treatment choices. Further well-designed randomized controlled trials are needed to address current evidence gaps.
Earwax; Cerumen; Cerumenolytic; Ear drops; Earwax impaction; and Ear cleaning.
A Fusion of Machine Learning and Traditional Statistical Forecasting Models for Analyzing American Healthcare Expenditure
US healthcare expenditure rose from 5.0% of GDP in 1960 to 18.3% in 2021 far exceeding comparable high-income nations despite persistently inferior health outcomes, most notably lower life expectancy. Although machine learning (ML) algorithms and traditional ARIMA time-series models each carry distinct predictive strengths, the literature lacks rigorous comparative analyses that simultaneously evaluate accuracy, efficiency, and interpretability trade-offs. This study addresses that gap by benchmarking five modeling approaches: (1) Random Forest, (2) Gradient Boosting Machine, (3) Support Vector Regression, (4) ARIMA (0,1,1), and (5) a novel Hybrid Fusion model that integrates Bayesian feature selection with an ML ensemble and ARIMA residual correction, drawing on 62 years (1960–2021) of annual US healthcare expenditure data. National Health Expenditure Accounts (NHEA) data from the Centers for Medicare & Medicaid Services (1960–2021; N=62 annual observations) were used. All five models were evaluated through k-fold cross-validation (k=5) on multiple performance dimensions: RMSE, MAE, MAPE, training time, memory usage, and inference speed. Random Forest achieved lowest RMSE (0.297%) among individual ML algorithms, forecasting 18.81% healthcare spending for 2050 compared to ARIMA(0,1,1) at 17.92% (RMSE 0.456%). Support Vector Regression demonstrated severe overfitting (RMSE 12.75%), confirming its unsuitability for small datasets (N=62). Novel hybrid fusion approach achieved 0.261% RMSE, 12% improvement over Random Forest alone. Linear regression analysis confirmed statistically significant annual healthcare spending growth of 0.239% GDP (t=107.79, p<2.16E-70) across full 62-year period. Ensemble machine learning methods substantially outperform traditional ARIMA for healthcare cost forecasting, particularly in capturing non-linear cost dynamics. Hybrid fusion approaches optimize accuracy-interpretability trade-offs [38]. Practical decision frameworks guide healthcare organizations in algorithm selection based on organizational priorities: accuracy-critical contexts favor Random Forest/Hybrid Fusion; real-time systems benefit from ARIMA; balanced requirements suit standard RF implementation.
Healthcare cost prediction, machine learning, ARIMA forecasting, Random Forest, Support Vector Regression, hybrid ensemble methods, comparative analysis, healthcare expenditure forecasting.
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The Impact of Transformational and Transactional Leadership Styles on Job Satisfaction among Healthcare Workers in Oman: A Systematic Review
Healthcare leadership is a central factor influencing workforce satisfaction, retention and service quality, particularly in health systems undergoing reform. Effective leadership, workforce stability and culturally responsive management is a value that has been introduced in Oman through healthcare transformation as part of Oman Vision 2040. A systematic review was done to establish the impact of transformational and transactional leadership styles on job satisfaction among Omani healthcare workers. The research used was systematic review that was guided by the PICO framework. The population of interest was Omani healthcare workers, the exposure was transformational and transactional leadership, the comparison was other leadership/management styles, and the outcome was job satisfaction. An evidence base provided went through a PRISMA-informed selection process. Thirty-eight records were identified and screened, 28 full-text reports were assessed for eligibility, and 15 studies were included in the narrative synthesis. The evidence included was categorized into direct Oman healthcare evidence, GCC healthcare evidence, international healthcare evidence and indirect organisational evidence. Quality of the studies was evaluated based on CASP-based principles of appraisal, such as the study design, relevance, sampling, measurement, bias and transferability. Meta-analysis was not done because the studies vary greatly in terms of design, population, outcome measures and statistical reporting. The findings indicated that transformational leadership was more effective in predicting positive job satisfaction particularly through motivation, communication, recognition, professional development, trust and organisational commitment. The correlation between transactional leadership and job satisfaction was conditional; it can help to increase satisfaction when applied to increase clarity of roles, accountability and fair feedback, but can decrease satisfaction when applied in a controlling or punitive manner. The review has also determined that there are wider organisational influences like workload, supervision, communication, pay, promotion and organisational culture, which influence job satisfaction. The review concludes that the healthcare industry in Oman needs to concentrate on culturally oriented leadership development that entails transformational support through appropriate transactional structure. The longitudinal and intervention studies unique to Oman must be carried out in the future to strengthen the causal evidence to help in meta-analysis in the future.
Transformational leadership, transactional leadership, job satisfaction, healthcare workers, Oman, systematic review.
Human-Centered Artificial Intelligence for Healthcare, Education, Business, and Assistive Technologies
Artificial intelligence increasingly mediates consequential human decisions across healthcare, education, business, assistive technologies, digital health, and organizational management. Yet the dominant evaluation paradigm—predictive accuracy—inadequately captures the properties most relevant to human welfare: interpretability, usability, accessibility, fairness, privacy, and the quality of human-AI collaboration. Human-centered AI reframes AI development as a socio-technical design discipline in which model outputs must support—rather than supplant—human expertise, judgment, and agency. This structured critical review synthesizes an eight-axis human-centered taxonomy covering domain, function, data modality, architecture family, design concern, deployment pathway, evidence role, and evidence maturity. Seven human-centered domains are examined: healthcare and biomedical decision support, education and adaptive learning, assistive technologies and accessibility, neuro-affective and mental-health AI, business and organizational decision-making, human-facing IoT and smart infrastructure, and cybersecurity and trustworthy digital systems. Synthesis reveals that while AI architectures have advanced substantially across vision transformers, hybrid ensembles, graph neural networks, and federated systems, human-centered design properties—validated explainability, inclusive design, privacy-preserving deployment, affective ambiguity management, and governance accountability—remain inconsistently addressed. An eleven-direction research agenda emphasizes human-centered evaluation, user-driven validation, fairness-aware design, accessible deployment, and governance-ready AI systems across all critical domains.
privacy-preserving AI, federated learning, edge-cloud computing, distributed intelligence, scalable decision support, critical applications, trustworthy AI, deployment readiness
Deployable AI Systems for Healthcare, Industry, Business, and Smart Infrastructure: A Cross-Domain Review
Artificial intelligence has progressed from laboratory benchmark systems to operational tools embedded in healthcare diagnostics, industrial monitoring, business analytics, smart infrastructure, agriculture, cybersecurity, and assistive technologies. However, the transition from a high-performing model to a deployable AI system remains a persistent challenge: clinical, industrial, and organizational deployment requires far more than predictive accuracy, demanding workflow integration, data interoperability, real-time feasibility, explainability, privacy, security, governance, and sustained post-deployment maintenance. This structured critical review synthesizes using a six-axis deployment-centered taxonomy encompassing application domain, data modality, architecture family, deployment pathway, deployment-readiness concern, and decision-support function. Seven application domains are examined: healthcare and biomedical AI, human-centered and assistive AI, industrial monitoring and cyber-physical systems, smart infrastructure and IoT, agriculture and sustainability, business and enterprise analytics, and cybersecurity and distributed intelligence. Eight architecture families are characterized, from conventional machine learning and CNNs through vision transformers, graph neural networks, Bayesian physics-guided models, generative AI, and federated learning systems, each associated with distinct deployment constraints and trustworthiness requirements. Synthesis identifies recurrent cross-domain deployment gaps including validated explainability, uncertainty quantification, privacy-preserving inference at scale, lightweight edge deployment, evidence maturity, and governance-aligned reporting. A structured future research agenda addresses these gaps with actionable directions and evaluation requirements. This review provides researchers, engineers, and practitioners with a deployment-focused roadmap for building AI systems that are not only capable but trustworthy, sustainable, and ready for real-world use.
Deployable AI, AI deployment, Clinical AI, Edge AI, Federated learning; Explainable AI, Industrial AI, Cross-domain deployment taxonomy.
AI-Enabled Resilient Systems: Healthcare, Sustainability, Cybersecurity, Energy, and Organizational Decision-Making
Resilience, the ability of a system to support continuity, adaptation, risk awareness, and recovery under uncertainty or disruption, has emerged as a central requirement for AI-enabled decision systems across critical domains. Healthcare systems must sustain diagnostic accuracy and patient privacy under data constraints and clinical heterogeneity. Energy and infrastructure systems must maintain monitoring reliability under sensor drift and communication instability. Cybersecurity systems must protect data and detect threats in adversarial environments. Agricultural and sustainability systems must function in variable field conditions with constrained hardware. Business and organizational systems must remain agile under economic volatility and pressure of governance. Human-centered AI systems must preserve accessibility, personalization, and ethical oversight for vulnerable users. This structured critical review synthesizes a curated corpus using a seven-axis resilience taxonomy encompassing resilience domain, resilience function, data modality, architecture family, system layer, deployment concern, and decision-support level. Seven resilience domains are examined and mapped across architecture families from conventional ML and CNNs through vision transformers, graph neural networks, physics-guided Bayesian models, generative AI, and federated privacy-preserving systems. Synthesis reveals that while AI capability has advanced substantially, resilience-specific properties robustness, uncertainty quantification, privacy-preserving collaboration, post-deployment monitoring, and governance accountability—remain inconsistently addressed. A twelve-direction research agenda addresses these gaps with actionable future directions and evaluation requirements for AI-enabled resilient systems.
AI resilience; Healthcare AI, Sustainability AI, Cybersecurity AI, Federated learning, Trustworthy AI, Organizational decision-making, Cross-domain AI taxonomy
Magnetic Resonance Imaging Patterns of Complex Anal Fistula: A Case Series- MRI of complex anal fistula
Complex anal fistula requires accurate preoperative mapping because unrecognized secondary tracts, supralevator extension, and abscesses increase the risk of persistent sepsis and recurrence. Magnetic resonance imaging (MRI) is central to defining fistula anatomy and guiding surgical planning. Case presentation: We describe four male patients aged 17-46 years who presented with persistent or recurrent perianal discharge. Pelvic MRI demonstrated transsphincteric fistulas in all cases. The imaging spectrum included multiple internal openings, bilateral external openings, branching secondary tracts, extension to the levator ani, pelvic sidewall or periprostatic region, circumferential perirectal inflammatory collections, perineal abscess close to the corpus spongiosum, gas-containing fistulous tracts, and horseshoe abscess involving the intersphincteric and supralevator spaces. MRI consistently depicted low signal intensity on T1-weighted images, high signal intensity on T2-weighted and fat-suppressed T2-weighted images, and marked rim or wall enhancement after gadolinium administration. Surgical management was individualized and included abscess drainage, excision of fibrotic fistula tracts, seton placement, and laser fistula treatment. Conclusion: This case series highlights the wide anatomical variability of complex anal fistulas and the practical value of MRI in demonstrating occult branches and abscesses. Structured MRI reporting using clock-face location, relationship to sphincters and levator ani, internal and external openings, and associated collections can improve communication between radiologists and surgeons.
Anal fistula; perianal fistula; magnetic resonance imaging; transsphincteric fistula; horseshoe abscess
Healing Beyond Medicine: Lived Experiences of Individuals Seeking Folk Healing in Kinabuhayan Dolores, Quezon Province
Folk healing remains a significant component of health-seeking behavior in many Filipino communities, where spiritual beliefs, cultural traditions, and social relationships shape perception of illness and recovery anchored in Madeleine Leininger’s Transcultural Nursing Theory and Health Belief Model, which explains how beliefs influence health-related decisions, this study explored the lived experiences of individuals seeking folk healing in Kinabuhayan, Dolores, Quezon Province, a community known for its sacred healing traditions associated with Mt. Banahaw. A qualitative Hermeneutic Phenomenological design was utilized to capture the meaning of the participants attributed their healing joints. Twelve (12) adult participants aged thirty (30) years and above were selected through purposive sampling based on their direct experience with folk healing practices. Data were collected through semi-structured interviews and analyzed using Max Van Manen’s phenomenological approach, employing holistic, selective, and detailed reading to interpret the essence of participants' narratives. Findings revealed that individuals were drawn to folk healing due to family traditions, economic barriers to biomedical care and chronic recurring illnesses, and spiritual beliefs regarding illness causation. Participants describe healing as a holistic experience and shaped by ritual practices, sacred landscape, and trusting healer-seeker relationships. Recovery was interpreted through embodied signs of improvement, renewed strong and strengthened faith rather than biomedical diagnosis. Participants also demonstrated a flexible approach to health-seeking by navigating between folk and biomedical systems. This study is limited by its sample size and focus on a single community which may limit transferability. Nevertheless, the findings contributed to a deeper understanding of culturally-grounded health practices and highlighted the importance of culturally-sensitive and holistic nursing care that respectfully bridges traditional and biomedical health systems.
Folk Healing; Experiences; Individuals; Kinabuhayan
Autoantibody-Mediated Synaptopathy: Redefining Neuropsychiatric SLE as a Disorder of Neuroimmune Synapse
Neuropsychiatric systemic lupus erythematosus (NPSLE) constitutes a severe and often refractory dimension of SLE, manifesting as clinically heterogeneous syndromes—including cognitive impairment, psychosis, and mood disorders—that follow a relapsing–remitting course. Vascular and inflammatory mediator models were developed to explain focal NPSLE and global encephalopathy, respectively. They were never designed to account for the circuit specific, often reversible symptoms of diffuse NPSLE—and they should not be faulted for failing to do so. The fault lies in our continued reliance on them as exclusive frameworks. Converging evidence now positions pathogenic autoantibodies targeting neuronal surface antigens as direct effectors of synaptic dysfunction. This Review synthesizes recent advances to propose a unifying ‘synaptopathy’ model for diffuse NPSLE pathogenesis. We propose the following four-step pathogenic cascade: (i) autoantibody access to the CNS following blood–brain barrier (BBB) compromise; (ii) acute synaptic dysfunction via receptor binding; (iii) complement-dependent synaptic tagging, amplified by DAMPs such as HMGB1; and (iv) irreversible synaptic loss driven by microglial pruning. These four steps define distinct therapeutic windows: steps (i) and (ii) are potentially fully reversible, step (iii) marks the transition to structural tagging, and step (iv) represents fixed-circuit damage. We aligned these windows with stage-specific biomarker profiles and targeted interventions. We then mapped these cellular events onto specific neural circuit disruptions (hippocampal, prefrontal-limbic, and cortico-thalamo-cortical), directly linking molecular pathology to clinical phenotypes. We operationalize this framework into a precision medicine strategy: three CSF biomarker profiles distinguish reversible synaptic dysfunction from irreversible structural loss, and a stage-matched therapeutic algorithm—B-cell depletion for antibody-driven disease, complement inhibition for synaptic tagging, and neuromodulation for fixed circuit deficits—is aligned with the four-step cascade. This framework reframes diffuse NPSLE as a mechanistically tractable disorder of the neuroimmune synapse—a conceptual advance that opens new avenues for biomarker discovery and targeted intervention.
Systemic Lupus Erythematosus, Neuropsychiatric Lupus, Autoantibodies, NMDA Receptor, Synaptic Pruning, Microglia
Systemic Congestion in a Young Patient: A Case of a Rare Infiltrative Disease
We report the case of a 26 year old male with no known chronic medical illness who presented to the emergency department with progressive shortness of breath and generalized body swelling over two weeks. Symptoms began gradually with reduced exercise tolerance and mild lower limb edema, followed by worsening breathlessness and abdominal distension. He had no history of chest pain, fever, recent infection, or prior cardiac or renal disease. There was no relevant family history of cardiomyopathy or inherited disorders. On arrival, he was hemodynamically stable but clinically congested with elevated jugular venous pressure, bilateral basal crackles, peripheral edema, and ascites. Point of care ultrasound demonstrated a dilated inferior vena cava with minimal respiratory variation and abdominal fluid, while chest radiography showed cardiomegaly with pulmonary congestion. Initial investigations showed mild renal impairment and elevated natriuretic peptides, supporting a congestive state, while electrocardiography revealed sinus tachycardia without ischemic changes. Given the patient’s young age and severity of presentation, the findings were considered atypical for conventional heart failure, raising suspicion for an underlying cardiomyopathy, particularly an infiltrative or storage disorder. The patient was started on intravenous diuretics with fluid and salt restriction, resulting in partial clinical improvement, although significant congestion persisted. Further evaluation at a tertiary center included transthoracic echocardiography and cardiac magnetic resonance imaging, which demonstrated features consistent with an infiltrative cardiomyopathy. Subsequent metabolic and genetic testing confirmed Fabry disease as the underlying diagnosis. Enzyme assays showed reduced alpha galactosidase A activity, and genetic analysis confirmed the diagnosis. This case highlights an uncommon presentation of Fabry disease manifesting as severe systemic congestion in a young adult with no prior medical history. The presence of marked jugular venous distension, dilated inferior vena cava, cardiomegaly, and ascites provided important early clues to significant cardiac involvement. Early recognition of an atypical pattern of heart failure in a young patient was essential in prompting further evaluation for infiltrative disease. The case emphasizes the importance of considering Fabry disease in young patients presenting with unexplained congestion and cardiomyopathy. Early use of bedside ultrasound and echocardiography played a key role in identifying the severity of volume overload and guiding further investigation. Confirmation of the diagnosis allowed initiation of disease specific therapy, including consideration of enzyme replacement treatment. In conclusion, Fabry disease should be considered in the differential diagnosis of young patients presenting with severe systemic congestion, particularly when imaging and clinical findings suggest cardiomyopathy without conventional risk factors. Early diagnosis is essential to allow timely treatment and prevent progressive organ damage.
Volume Overload, Systemic Congestion, Jugular Venous Pressure, Heart Failure, Cardiomyopathy, Infiltration, Fabry Disease.
Rare Coexistence of Abdominal Aortic Aneurysm and Pericardial Effusion: A Case Report.
We report the case of a 59-year-old male who presented to the emergency department with a two-week history of progressive shortness of breath, intermittent fever, fatigue, and reduced exercise tolerance. He later developed non exertional central chest discomfort, vague abdominal pain, and poor appetite with unintentional weight loss. There was no history of recent invasive procedures or known cardiac disease. On presentation, he was febrile, tachycardic, and mildly hypoxemic, with elevated jugular venous pressure, bilateral basal crackles, and a subtle early diastolic cardiac sound. Abdominal examination revealed a suspected pulsatile mass. Initial investigations showed raised inflammatory markers and leukocytosis. Chest radiography demonstrated cardiomegaly, while bedside ultrasound revealed a moderate pericardial effusion and a fusiform infrarenal abdominal aortic dilatation. Computed tomography of the abdomen confirmed an abdominal aortic aneurysm with features suggestive of infective involvement. Blood cultures later grew a streptococcal species consistent with infective endocarditis. The patient was managed with targeted intravenous antibiotics after initial empirical therapy, along with close hemodynamic monitoring. Cardiology input supported conservative management of the pericardial effusion as there were no signs of tamponade. Vascular surgery recommended initial non operative management of the suspected mycotic aneurysm with strict blood pressure control and prolonged antibiotic therapy. Multidisciplinary care was central to management, including infectious diseases, cardiology, and vascular surgery teams. This case highlights an unusual presentation of infective endocarditis complicated by both pericardial effusion and abdominal aortic aneurysm, emphasizing the importance of early imaging, blood culture diagnosis, and multidisciplinary coordination in detecting and managing systemic infectious vascular complications.
Aortic Aneurysm, Pericardial effusion, Arteritis, Vasculitis, Serositis, Pericarditis, Infective Endocarditis.
Formulation, In-vitro and Antifungal Activity of Fluconazole by Absorbant Solid Dispersion
Fluconazole is a antifungal drug used for numerous systemic and artificial fungal infections. However, it has many boundaries when directed in the form of oral solid dosage forms due to its poor aqueous solubility and dissolution profile, which is a major effort in reaching adequate oral bioavailability. Adsorbent solid dispersion (ASD) is one of the highly promising methods for ameliorating the wettability and solubility of medications having low aqueous solubility. The current research aims to improve the dissolution and solubility profile of fluconazole using the ASD approach. Polyethylene glycol 6000 (PEG 6000), Poloxamer 188 and Poloxamer 407 were applied as water- soluble polymeric carriers to trap the drug in an amorphous state and improve dissolution profile. In addition, Aerosil 200 and Aerosil 300 were utilized as highly porous carriers (adsorbents) to drastically increase the drug's contact area with dissolution media. Six adsorbent solid dispersion formulas (ASDF1 - ASDF6) were prepared using the melting method in a ratio of 0.25:0.25:0.25 (drug: carrier: adsorbent). The prepared mixtures were evaluated for saturated solubility, drug content, percentage yield, in-vitro release and antifungal activity. The prepared formulations showed an improvement in the drug's solubility. The greatest result was achieved with formulation Flu4(Poloxamer407, Aerosil 3000 and Fluconazole), which showed an increase in solubility associated with pure fluconazole. This enhancement is attributed to the alteration of the drug into an amorphous state and the massive surface area providing by the porous carriers.
Fluconazole, Adsorbent solid dispersions, PEG 6000, Poloxamer 407, Poloxamer 188, Aerosil 300, Aerosil 200.
Epidemiological Trends of Healthcare-Associated Infections in Chile: A National Descriptive Study (2015–2022)
Healthcare-associated infections continue to represent a significant challenge for healthcare systems, particularly in resource-constrained settings. Evaluating national trends is essential to support infection prevention and control strategies, especially during periods of increased healthcare demand such as the COVID-19 pandemic. We conducted a retrospective ecological analysis using aggregated national surveillance data from medium- and high-complexity public hospitals in Chile between 2015 and 2022. Temporal trends in major HAIs, including ventilator-associated pneumonia (VAP), catheter-associated urinary tract infections (CAUTI), central line–associated bloodstream infections (CLABSI), and surgical site infections (SSI), were analyzed using descriptive statistics. A total of 43,478 HAIs were reported. Gram-negative microorganisms predominated overall, particularly in VAP and CAUTI, while Gram-positive organisms predominated in CLABSI and SSI. Although the total number of infections increased during the COVID-19 pandemic, the proportional distribution of major etiological groups remained relatively stable over time. Despite increased infection burden during the pandemic period, national surveillance data suggest stability in the overall distribution of major etiological groups. These findings highlight the importance of sustained surveillance systems to monitor infection trends and support infection control strategies in developing countries.
Ethical Governance of Bioinformatics and Genomic AI Systems: From Compliance to Institutional Legitimacy
Bioinformatics and genomic artificial intelligence (AI) systems are increasingly embedded in clinical and organizational decision-making. However, ethical analysis in this area remains dominated by regulatory compliance, privacy protection, and technical control. Although these approaches are necessary, they are analytically insufficient because they do not adequately explain how ethical governance is enacted within organizations, how technological practices mediate governance, or why formally compliant systems may still fail to achieve institutional acceptance. This article develops a conceptual framework through interdisciplinary analysis of biomedical informatics governance scholarship, socio-technical systems theory, organizational theory, and legitimacy theory. Its aim is not merely to synthesize these literatures, but to address an explanatory gap concerning how ethical governance is organizationally produced and how its effectiveness should be evaluated. The proposed Culture–Technology–Ethics–Legitimacy (CTEL) framework links four interdependent domains: organizational culture, bioinformatics technology practices, ethical governance practices, and legitimacy outcomes. The framework makes three conceptual contributions. First, it redefines ethical governance as a cross-level socio-technical process rather than a compliance endpoint. Second, it conceptualizes technology as an ethical mediator through which organizational norms are translated into operational practice. Third, it positions legitimacy as the key downstream outcome of governance and distinguishes scientific, clinical, social, and moral legitimacy as analytically distinct but interacting forms of institutional acceptance. By repositioning legitimacy as the central evaluative outcome of ethical governance, the framework advances biomedical informatics ethics beyond compliance-driven approaches and offers a stronger explanatory account of why some bioinformatics and genomic AI systems become institutionally trusted while others do not. The framework therefore offers a distinct conceptual contribution and a foundation for future empirical investigation
Bioinformatics; Genomic AI; Ethical governance; Legitimacy; Organizational culture; Biomedical ethics
Rule-Based Artificial Intelligence for Health Information Management, Governance, and Healthcare Quality
Health information governance weaknesses can create operational risks in digital healthcare environments, including incomplete documentation, duplicate patient records, weak access control, poor interoperability, absent audit trails, and delayed reporting. These risks may affect data quality, information security, care continuity, accountability, reporting reliability, healthcare quality, and community-level planning. This paper proposes an artificial intelligence rule-based information governance framework for classifying such risks in health information management and health information systems. Using a scenario-based design-science approach, the framework defines governance input variables, maps them to health information management and health information system mechanisms, applies explicit IF-THEN rules, assigns risk classes, generates explanation traces, and produces governance recommendations. The framework is demonstrated through six synthetic scenarios covering duplicate records, unauthorized access, incomplete referral information, missing clinical documentation, weak audit trails, and delayed public health reporting. The demonstration shows how governance weaknesses can be converted into structured inputs, interpretable risk outputs, and corrective actions. The study does not claim empirical validation or deployment as a software system. Instead, it specifies a structured design artifact that can support future simulation, expert validation, rule-engine implementation, and empirical testing using real health information system data
Rule-Based Artificial Intelligence; Health Information Governance; Health Information Management; Health Information Systems; Healthcare Quality; Scenario-Based Analysis
Management of Vascular and Ischemic Complications in Aesthetic Injectables: A Retrospective Study of 52 Consecutive Cases and Proposal of a Standardized Rapid-Response Protocol
Vascular occlusion and ischemic complications after aesthetic injectable procedures are uncommon but may be disastrous, and can result in skin necrosis, permanent scarring, blindness or stroke. In this retrospective study, 52 consecutive real-world cases were analyzed and treated between January 2022 and December 2025 and assessed the safety and efficacy of a new standardized Rapid-Response Protocol created by the author. The MARYNA Rapid-Response Protocol of immediate high-dose pulsed hyaluronidase (450-1500 IU, every 30-60 minutes) combined with vigorous massage, universal antiplatelet therapy, ultrasound guidance when available, adjunctive vasodilators, and dedicated ocular emergency pathway were used to treat all cases of vascular or ischemic complications (primarily hyaluronic acid fillers, 92.3%). Clinical and photographic outcomes were measured at standardized intervals at a maximum of 90 days. Early intervention (within 4 hours of symptom onset) occurred in 73.1% of cases and resulted in 100% full recovery without permanent sequelae. In total, 94.2% of patients had no cutaneous necrosis, scarring, blindness, or cerebrovascular events and achieved full recovery. The mean hyaluronidase dose was 875 IU. Late presentations (>12 hours) were associated with a slightly increased rate of mild atrophic scarring (5.8% overall). There were no adverse reactions to hyaluronidase. The MARYNA Rapid-Response Protocol provides superior practical results and provides a basic and easily reproducible, evidence-based algorithm that can be immediately embraced by aesthetic practitioners around the globe. These results confirm that aggressive, timely and standardized intervention can minimize the permanent complications of aesthetic injectables to the levels of near zero, which is the new standard of patient safety in the area.
Vascular occlusion, dermal fillers, hyaluronic acid, hyaluronidase, ischemic complications, skin necrosis, rapid-response protocol, aesthetic injectables, ultrasound-guided reversal, ocular emergency
Assessing the Nexus between Digital Maturity and Institutional Accountability in Bangladesh’s Public Health System: A 2022 Cross-Sectional Analysis
In the 2022 post-pandemic landscape, the digitalization of health services in the Global South has transitioned toward increased institutionalization. This research investigates the statistical association between perceived facility-level digital governance maturity and institutional accountability across a stratified sample of 450 public healthcare facilities in Bangladesh. Employing a cross-sectional quantitative design, the study sampled health administrators across eight divisions to evaluate digital literacy, infrastructure maturity, and policy knowledge as observed correlates of accountability performance. Data were analyzed using Ordinal Logistic Regression (OLR). Results indicate that higher levels of digital literacy (OR=3.64, 95% CI [2.89, 4.60]) and infrastructure maturity (OR=2.81, 95% CI [2.21, 3.58]) are positively associated with achievement of higher accountability tiers. These association patterns suggest a "policy dissemination gap," in which technical adoption has outpaced administrative understanding of data governance standards. While these findings suggest that digital maturity correlates with transparency, the study is limited by its cross-sectional design, reliance on self-reported administrative perceptions, and lack of external validation against objective system audit logs. The associations provide an empirical basis for refining the 2023-2027 Digital Health Strategy by prioritizing human capital and policy clarity alongside hardware expansion.
Artificial Intelligence, Big Data Analytics, Wearable Health, Multi-Omics, Precision Medicine, Predictive Modeling, Biomedical Innovation, Sustainability
Copyright (c) 2022 https://creativecommons.org/licenses/by/4.0/
Comparative analysis of explainable machine learning models for cancer classification using cytological features
Breast cancer is among the causes of cancer related deaths globally with the greatest impact being in the low resource and high volume health care facilities where timely and accurate screening is paramount. This research report is a explainable machine learning model of breast cancer diagnosis based on quantitative features of fine needle aspirate images of breast masses. The data set contains 569 samples and 30 real-valued predictors of cell nuclei morphology, and it does not contain missing values, and the class distribution is moderate. It uses a structured preprocessing pipeline, such as the division of data into training and held-out test sets, feature normalization, and the careful management of class imbalance.Several classification models are compared, among them, Random Forest and Gaussian Naive Bayes, to compare the predictive accuracy and reliability of the model. Experimental outcomes have shown that the Random Forest model obtains the best performance with an accuracy of 0.96 on the held out test set, and balanced precision and recall on the benign and malignant classes. The confusion matrix shows that there is low misclassification rate and only false positive and false negative are three and three respectively. Contrastingly, Gaussian Naive Bayes has a higher accuracy of 0.93, and is less sensitive to malignant cases because of its independence assumptions which are not completely met in the dataset as verified by correlation analysis. These results are also supported by receiver operating characteristic analysis, whose area under the curve value is 1.00 in random forest and 0.99 in Gaussian naive bayes.The findings emphasize the role of model selection in clinical decision support systems, especially in cases where false negatives have to be minimized. The suggested structure focuses on interpretability and high practical use thus appropriate in deployment-oriented screening processes in resource-constrained settings. This paper shows that explainable machine learning models are trained on structured cytological features and can give effective and reliable support in early detection of breast cancer.
Breast cancer classification, explainable machine learning, fine needle aspirate cytology, cytological feature analysis, Random Forest, Gaussian Naive Bayes
Diagnostic Value of Ultrasound-Guided Core Needle Biopsy in the Diagnosis of Pulmonary Masses at Ho Chi Minh City Oncology Hospital
Histopathological confirmation is essential for the diagnosis of pulmonary masses. Although computed tomography (CT)-guided transthoracic biopsy is widely used, it remains associated with a relatively high complication rate, especially pneumothorax. Ultrasound-guided core needle biopsy is a practical alternative for peripheral lung lesions abutting the pleura. This study aims to evaluate the diagnostic performance and safety of ultrasound-guided core needle biopsy for pulmonary masses at Ho Chi Minh City Oncology Hospital. A cross-sectional descriptive study was conducted in 136 consecutive patients with peripheral pulmonary masses visible on ultrasound who underwent ultrasound-guided core needle biopsy from July 2024 to February 2025. Demographic characteristics, lesion size, depth, pleural contact length (PCL), procedure time, number of tissue cores, pathological results, and complications were analyzed. The mean age was 62.3 ± 11.1 years, and 72.8% of patients were male. Definitive histopathological diagnosis was achieved in 128/136 cases (94.1%). Malignancy was identified in 107 cases (78.7%), including 90 primary lung malignancies and 17 metastatic lesions. Mean procedure time was 15.6 minutes, and a mean of 4.9 ± 1.17 cores was obtained per patient. Pneumothorax occurred in 10 patients (7.4%), all mild and self-limited, and mild hemoptysis occurred in 2 patients (1.5%). No severe complications or procedure-related deaths were observed. Ultrasound-guided core needle biopsy is a safe, efficient, and accurate technique for diagnosing peripheral pulmonary masses. It provides a high diagnostic yield with a low complication rate and is a valuable first-line biopsy approach for appropriately selected pleural-based lung lesions.
ultrasound-guided biopsy; core needle biopsy; lung mass; peripheral pulmonary lesion; diagnostic yield; pneumothorax
Therapeutic Effects of “Phu Thao Khang – PTP” Solution in the Treatment of Vaginitis and Cervicitis Caused by Mixed Bacterial Infection
This study evaluated the adjunctive effect of Phu Thao Khang-PTP solution on selected paraclinical outcomes in women with vaginitis and cervicitis caused by mixed bacterial infection. An open-label controlled clinical intervention was conducted in 60 sexually active women aged >18 years who were diagnosed with vaginitis and/or cervicitis at Tue Tinh Hospital from April 2025 to March 2026. Participants were allocated into a study group receiving daily genital cleansing with Phu Thao Khang-PTP plus one Polygynax vaginal capsule daily for 10 days, or a control group receiving Polygynax alone for 10 days. Outcomes were assessed at baseline (D0) and day 10 (D10) using cervical examination, vaginal discharge characteristics, Candida testing, and Gram staining. After treatment, the study group achieved higher rates of normalization of cervical findings (86.7% vs. 56.7%), Candida clearance (100% vs. 86.7%), Gram-positive bacterial clearance (96.7% vs. 83.3%), Gram-negative bacterial clearance (93.3% vs. 76.7%), and overall grade A response (83.3% vs. 56.7%). No adverse effects were observed. Phu Thao Khang-PTP combined with Polygynax may be an effective and safe adjunctive treatment for mixed vaginitis and cervicitis.
Vaginitis; Cervicitis; Mixed bacterial infection; Phu Thao Khang-PTP; Polygynax
Factors Influencing Infection in Intracerebral Hemorrhage Patients: A Literature Review
Infectious complications represent the primary non-neurological challenge for individuals diagnosed with Intracerebral Haemorrhage (ICH), acting as a major driver for unfavourable clinical results, extended ICU stays, and heightened healthcare costs. This literature review offers a systematic analysis of the varied elements that contribute to infection in ICH cases, spanning from innate physiological vulnerabilities to risks acquired within the hospital setting. By integrating findings from 20 contemporary journals (2021–2026) and 3 core medical textbooks, this research highlights essential risk determinants such as diminished Glasgow Coma Scale (GCS) scores, total hematoma volume, and metabolic imbalances like hyperglycaemia upon admission. Furthermore, the review investigates Stroke-Induced Immunodepression Syndrome (SIDS) as a fundamental biological mechanism. These insights emphasize the urgency of early risk categorization and focused preventive measures to curb infectious setbacks in specialized clinical environments, such as Universitas Airlangga Hospital.
Intracerebral Hemorrhage, Post-Stroke Pneumonia, Risk Determinants, Immunosuppression, Literature Review, Hospital- Acquired Infection