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Hospital-acquired pneumonia is typically polymicrobial; nevertheless, Pseudomonas aeruginosa is a principal causative pathogen, attributable to its link with poor clinical prognoses and extensive antimicrobial resistance. Our study aims to assess the prevalence, microbiological profiles and determinants of hospital-acquired pneumonia with a focus on antibiotic-resistant P. aeruginosa across three hospitals in Addis Ababa. A cross-sectional study was conducted in which 1,800 patients were screened, and 298 cases of hospital-acquired pneumonia were identified between September 2022 and April 2024. Patient interviews and microbiological analysis of lower respiratory tract samples were performed. We detected a 17% prevalence of hospital-acquired pneumonia and 19% prevalence of ventilator-associated pneumonia across the study hospitals. Our patient profiles indicated a predominance of males (59%), with the largest proportion aged 30-39 years (28%), most were married (71%) and had attained secondary-level education (33%). Over half of the patients were admitted to the adult ICU (55%), 60% had a history of prior hospitalization and respiratory disease was the leading cause of admission (30%). Acinetobacter baumannii (n = 24) was the most frequently isolated pathogen, followed by Pseudomonas aeruginosa (n = 21) and Staphylococcus aureus (n = 13). Compounding these challenges, the P. aeruginosa isolates (7%) exhibited high resistance to ceftazidime and cefepime (89% resistance), while retaining relatively high susceptibility to amikacin (90%); notably, 67% of the isolates were multidrug resistant. We tested several patient-level vulnerabilities, only aspiration remained independently associated with presence of pneumonia-associated pathogen in patient samples (AOR = 4.43, 95% CI: 1.74-11.24, p = 0.002). This study demonstrates a substantial burden of multidrug resistance hospital-acquired pneumonia by ESKAPE pathogens that indicate deficiencies in hospital defences against hospital-acquired pathogens and risk of adverse patient outcomes. There is an urgent need to shift infection prevention strategies, emphasizing aspiration prevention measures and strengthened diagnostic stewardship.

Background: The white blood cell-to-hemoglobin ratio(WBC/Hb), composite marker derived from routine laboratory parameters, may offer unique prognostic value by integrating systemic inflammation and physiological reserve. This study investigates its association with inhospital mortality(IHM) in a resource-limited medical setting.

Methods: We retrospectively enrolled patients admitted to medical ward of Naguru regional referral Hospital-Uganda, between January and June 2024. Data on demographics, clinical status, and lab results, including WBC-count and hemoglobin, were extracted on admission. The primary outcome was IHM. Patients were categorized into three WBC/Hb subgroups. Hazard ratios(HR) and Area Under the Curve(AUC) assessed its prognostic value, adjusting fully for age, sex, comorbidities, and admission diagnoses.

Results: Overall, 226 patients were included(mean age 45.35 ± 18.85yrs, 54.4% female). The mean WBC/Hb ratio was 1.04 ± 1.22 × 10⁹ cells/L per g/dL, and IHM rate was 19.9%. Per-standard increase of WBC/Hb(2.22 × 10⁹ cells/L per g/dL) was associated with high-risk of IHM (HR 1.19, 95% CI 1.00-1.44; p = 0.012). The Results were similar when stratified into three groups (<0.55, 0.55-1.00, and ≥1.00 × 10⁹ cells/L per g/dL), compared with the reference group(<0.55), 0.55-1.00 group (HR 2.81, 95% CI 1.06-7.43; p = 0.037) and ≥1.00 group (HR 2.82, 95% CI 1.05-7.57; p = 0.040) had significantly high-risks of IHM. WBC/Hb demonstrated predictive value for IHM with AUC of 0.701 (95% CI 0.550-0.718).

Conclusion: WBC/Hb, readily available and cost-effective marker, was associated with IHM. Incorporated into routine clinical assessments could improve risk stratification, especially in resource-limited settings. Prospective studies are needed to validate these findings and assess its broader utility.

The research creates a Vision Transformer (ViT) diagnostic system which identifies pneumonia and pneumothorax from chest radiographs through analysis of the NIH ChestX-ray14 dataset. The research methodology solves medical imaging problems through three essential components which include (i) radiograph-specific augmentation for simulating authentic imaging conditions and (ii) multi-label imbalance handling through WeightedRandomSampler with class-specific weight application to stop all-normal predictions and (iii) optimization improvements that include CosineAnnealingWarmRestarts scheduling and sigmoid-based classification head optimization and disease-specific threshold optimization. The evaluation of model performance uses AUC and sensitivity and specificity and precision and F1-score because accuracy proves ineffective when dealing with severe class imbalances. The ViT models achieve 70-75% accuracy and 0.63-0.71 AUC values for both target conditions during non-leaking and noise-aware experiments because of the weak labels and restricted supervision in the ChestX-ray14 dataset. The system enhances its ability to detect rare conditions while providing better interpretability through Vision Transformer attention-based visualization of important radiological areas. The research demonstrates that ViT performance improves significantly through medical-focused data preparation methods and training approaches which demonstrate potential for radiology assistance in high-volume and resource-constrained environments.

c-KIT is a transmembrane receptor tyrosine kinase involved in various signaling pathways. Alternative pre-mRNA splicing of KIT results in isoforms that differ in the presence or absence of four amino acid sequences in the extracellular juxtamembrane region, such as, isoforms with and without the GNNK sequence (GNNK+ and GNNK - , respectively) in humans and mice, and those with and without GNSK (GNSK+ and GNSK - , respectively) in domestic dogs, cats, and sheep. These isoforms have been extensively studied as disease-associated (particularly tumors or cancer) splice variants with differing kinase activities. However, the expression patterns and regulatory factors of each isoform in various animal species without tumors or cancer remain poorly understood. Studying these aspects can provide the basis for understanding the associations between c-KIT isoforms and disease. Therefore, in the present study, a comprehensive expression analysis of c-KIT isoforms was conducted using tissue-wide transcriptome data from humans, mice, dogs, cats, and sheep. We found that the expression ratio of c-KIT isoforms differs across tissues, and such features are conserved across animal species: GNNK+ and GNSK+ isoforms have high expression ratios in the central nervous system, while GNNK- and GNSK- predominate in other tissues. Furthermore, NOVA2, RBFOX1, RBFOX3, and DYRK1A were suggested to be candidate factors regulating the selection of the alternative 5' splice donor site of KIT.

[This corrects the article DOI: 10.1371/journal.pone.0312977.].

Background: Chronic pancreatitis (CP) can lead to pancreatic exocrine insufficiency (PEI), causing protein malabsorption and altered amino acid levels. Our aim is to investigate amino acid levels in patients with CP and to assess the associations with exocrine insufficiency, pancreatic elastography, and the severity of CP.

Methods: Twenty-five patients with CP were prospectively enrolled in this study. A control group included 28 age- and gender-matched subjects without pancreatic disease. Amino acids were quantified using the Biocrates MxP Quant 500 kit. Differences in amino acid levels were calculated using the non-parametric Wilcoxon test. Pancreatic stiffness was evaluated by EUS-elastography, PEI was assessed using fecal elastase-1 levels. The severity of CP was determined using the M-ANNHEIM classification.

Results: The ratio of nonessential amino acids to essential amino acids, glutamic acid and aspartic acid levels were significantly increased in the CP group, whereas tryptophan levels were significantly decreased in CP. A positive correlation between proteinogenic amino acids and the severity of pancreatitis was found. Proline levels showed a strong correlation with the CP severity index (r = 0.63), a significant correlation with elastase-1 values (r = -0.48, p = 0.02), and a moderate correlation with pancreatic stiffness (r = 0.47, p = 0.09).

Conclusion: Patients with CP have altered amino acid blood levels, an increased ratio of nonessential to essential amino acids. The severity of CP was found to increase the levels of nonessential amino acids, with proline showing the strongest associations with severity, elastase-1 levels, and pancreatic stiffness.

Image Quality Assessment (IQA) plays a critical role in image-based decision-making systems, especially in domains requiring high diagnostic precision. Effective feature information is a prerequisite for the high performance of machine learning methods in parasitic organism detection, and the quality of this feature information is influenced by the quality of the images. However, No-Reference IQA (NR-IQA) models have ignored microscopy-based datasets, particularly those involving parasitic organisms such as Cryptosporidium spp. and Giardia spp., which are vital for public health inspection. In this study, PRIQA (Parasite ResNet-101 IQA), a novel deep learning-based NR-IQA model specifically trained on a small parasite image dataset was presented. Using Mean Opinion Scores (MOS) from twenty human evaluators, nine Deep Convolutional Neural Network (DCNN) architectures were benchmarked and identified ResNet-101 as the most robust feature extractor. The features were mapped to MOS using regression models and compared with ten state-of-the-art NR-IQA algorithms. Experimental results demonstrated that PRIQA consistently outperforms existing methods, indicating its suitability as a practical quality control tool for identifying unreliable or low-quality parasite microscopy images and supporting more consistent downstream detection and diagnostic workflows in automated inspection systems.

Objective: Although health workforce equity has gained more attention, few studies have explored its spatial distribution and influencing factors in Inner Mongolia, a vast and diverse region of China. Existing researches often use simple geographic adjacency-based models that do not fully consider both location and economic factors. To address this, this study applies spatial econometric methods to examine the direct and indirect effects of influencing factors on health workforce distribution in Inner Mongolia from 2013 to 2022.

Methods: Data were obtained from the Inner Mongolia Statistical Yearbook (2013-2022). Health workforce (HW) was measured by the number of health professionals per 1,000 persons. Spatial distribution and clustering patterns were analyzed using Global and Local Moran's I. Four types of independent variables were selected: socioeconomic factors (per capita GDP and disposable income), demographic factors (population density and population growth), institutional environment (fiscal self-sufficiency rate), and supportive resources (beds density). A spatial panel econometric model was applied to assess both direct and indirect effects.

Results: Significant spatial clustering of HW was found throughout the study period. High-high clusters were concentrated in the Hohhot-Baotou-Erdos region, while low-low clusters appeared in remote rural and pastoral counties. The Spatial Durbin Model (SDM) was chosen to explore the influencing factors. Direct effects showed that disposable income and bed density positively influenced HW within a county, whereas population density exhibited a significant negative impact. Indirect effects revealed that disposable income, fiscal self-sufficiency rate, and bed density also had positive spatial associations on HW in neighboring regions.

Conclusion: Health workforce allocation in Inner Mongolia shows significant spatial disparities, with decreasing clustering over time, indicating reduced regional heterogeneity. Disposable income, bed density, and fiscal self-sufficiency positively affect HW and exhibit notable spatial associations, while population density has a negative impact. To optimize allocation, policies should enhance regional collaboration and resource sharing, increase fiscal support and promote medical alliances.

Remote photoplethysmography (rPPG) is a noncontact camera-based optical technique which analyses skin-color variations due blood volume changes beneath the skin and estimates vital signs such as heart rate (HR). Driven by the increasing demand for long-term unobtrusive vital sign monitoring systems, rPPG has grown significant interest in clinical and nonclinical domains. However, conventional rPPG methods are often limited by following challenges such as motion artifacts (MA), ambient light intensity and weak signal quality. Moreover, their overall accuracy is significantly impacted by demographic and skin-tone variations. To address these limitations, an advanced signal-processing framework integrating discrete wavelet transform (DWT) for denoising and signal-to-noise-ratio enhancement with residual-based adaptive Kalman filtering (RAKF) for frame-wise temporal consistency and MA reduction is proposed (DWT-RAKF). Further, a multi-channel fusion strategy is integrated with dual-stage band-pass filtering technique to isolate the HR signal while effectively discarding unrelated signal components. Our proposed framework is evaluated on both public and custom datasets. Regarding the PURE dataset, the proposed framework obtained a mean absolute error (MAE) and a root mean square error (RMSE) of 0.72 and 1.14 bpm respectively, outperforming several conventional state-of-the-art methods. To further evaluate its real-world performance, intra-dataset testing is implemented using custom dataset comprising subjects with varying skin-tones and under natural lighting conditions. The results revealed that the proposed algorithm obtained the lowest MAEs of 0.94 and 1.11 bpm for fair-skinned and dark-brown subjects respectively, indicating that the integration of the proposed signal filtering strategy with rPPG achieved effective real-time HR measurement.

Introduction: Long-acting reversible contraceptives (LARCs) are critical for reducing maternal mortality and unintended pregnancies, yet adoption remains low in Sub-Saharan Africa (SSA) due to systemic inequities, cultural barriers, and fragmented healthcare access. Despite global advancements, only 8% of women in SSA use LARCs, underscoring the need for data-driven insights to address this gap. This study applies machine learning (ML) to identify key predictors of LARC use and guide interventions.

Methods: Nationally representative data from 14,275 women across nine SSA countries were analyzed. Preprocessing included k-NN imputation and advanced class balancing (SMOTEENN). Feature engineering derived interaction terms (age×household size, education×media exposure) with SHAP-driven selection. Eight ML models were trained and hyperparameter-tuned using stratified cross-validation.

Results: After hyperparameter tuning and class balancing, Random Forest achieved excellent discriminative performance (AUC-ROC: 1.00). Key predictors were household size (SHAP = 0.464), age at first contraceptive use (0.396), and current age (0.376). Socio-cultural factors (religion, marital status) showed negligible impact and were excluded. LARC uptake remained critically low (3.3%) with persistent rural-urban disparities.

Conclusion and recommendations: The model's key predictors directly inform policy; we recommend: 1) Mobile clinics for young women in large households, targeting the two strongest negative predictors (young age and large household size), 2) Media campaigns tailored to educated populations, leveraging the significant interaction between education and media exposure, and 3) Adolescent-focused education on contraceptive timing, addressing the critical predictor of age at first use.