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Background This study aimed to evaluate the impact of optimized diagnostic protocols on pathogen detection rates in abscess specimens. Methods Our retrospective study analyzed 1,297 abscess specimens collected between 2018 and 2024 using an enhanced diagnostic protocol combining four key methodologies: routine aerobic/anaerobic culture, gram-stain microscopy, acid-fast bacilli staining, and blood culture bottle enrichment techniques. Results The implementation of optimized diagnostic protocols significantly enhanced pathogen detection efficacy ( P  < 0.001, χ = 9.663), achieving an overall positivity rate of 81.9% (1,062/1,297)—a 20.1 percentage point improvement over conventional methods. Among culture-positive specimens, polymicrobial infections were identified in 27.6% of cases (293/1,062). A total of 1,651 microbial isolates were recovered, dominated by gram-negative bacteria (50.6%, 836/1,651) with  Escherichia coli  (55.0%),  Klebsiella pneumoniae  (23.0%), and  Acinetobacter baumannii  (4.0%) as predominant species. Gram-positive cocci accounted for 33.7% (557/1,651), primarily  Streptococcus  spp. (45.0%),  Staphylococcus aureus  (19.0%), and  Enterococcus faecium  (6.0%). Enhanced methodology detected 261 additional pathogens (20.1% of total yield), including anaerobes (33.7%), smear-positive organisms (32.2%), acid-fast bacilli (6.9%), and  Brucella melitensis  (1.5%). Anatomic distribution analysis revealed perianal abscesses (358 cases, 407 isolates) predominantly associated with  E. coli  (51.8%),  K. pneumoniae  (14.7%), and  Streptococcus  spp. (15.7%), followed by maxillofacial infections (244 cases, 297 isolates; 18.0%). Other significant sites included abdominal abscesses/peritonitis (17.0%), hepatic abscesses (5.2%), and Periappendicular abscess (5.5%). Conclusions Systematic optimization of diagnostic protocols significantly enhanced pathogen detection in abscess specimens, demonstrating substantial clinical utility for infectious disease management. These findings support the adoption of comprehensive, standardized approaches for abscess specimen processing. Highlights Adopting optimized standardized operating procedures, abscess specimens were detected positive accounting for 81.9%, and the positivity rate increased by 20.1%. Additional abscess specimens were detected positive, including anaerobic bacteria, smear test, and acid-fast bacteria accounting for 33.7%, 32.2%, and 6.9%, respectively.

Background Pancreatic ductal adenocarcinoma (PDAC) is difficult to detect early and highly aggressive, often leading to poor patient prognosis. Existing serum biomarkers like CA19-9 are limited in early diagnosis, failing to meet clinical needs. Machine learning (ML)/deep learning (DL) technologies have shown great potential in biomedicine. This study aims to establish PDAC differential diagnosis and prognosis assessment models using ML combined with serum biomarkers for early diagnosis, risk stratification, and personalized treatment recommendations, improving early diagnosis rates and patient survival. Methods The study included serum biomarker data and prognosis information from 117 PDAC patients. ML models (Random Forest (RF), Neural Network (NNET), Support Vector Machine (SVM), and Gradient Boosting Machine (GBM)) were used for differential diagnosis, evaluated by accuracy, Kappa test, ROC curve, sensitivity, and specificity. COX proportional hazards model and DeepSurv DL model predicted survival risk, compared by C-index and Log-rank test. Based on DeepSurv’s risk predictions, personalized treatment recommendations were made and their effectiveness assessed. Results Effective PDAC diagnosis and prognosis models were built using ML. The validation set data shows that the accuracy of the RF, NNET, SVM, and GBM models are 84.21%, 84.21%, 76.97%, and 83.55%; the sensitivity are 91.26%, 90.29%, 89.32%, and 88.35%; and the specificity are 69.39%, 71.43%, 51.02%, and 73.47%. The Kappa values are 0.6266, 0.6307, 0.4336, and 0.6215; and the AUC are 0.889, 0.8488, 0.8488, and 0.8704, respectively. BCAT1, AMY, and CA12-5 were selected as modeling parameters for the prognosis model using COX regression. DeepSurv outperformed the COX model on both training and validation sets, with C-indexes of 0.738 and 0.724, respectively. The Kaplan-Meier survival curves indicate that personalized treatment recommendations based on DeepSurv can help patients achieve survival benefits. Conclusion This study built efficient PDAC diagnosis and prognosis models using ML, improving early diagnosis rates and prognosis accuracy. The DeepSurv model excelled in prognosis prediction and successfully guided personalized treatment recommendations and supporting PDAC clinical management.

Background There is limited information on surgical site infections (SSI) and the related antibiotic resistance needed to guide their management and prevention in Sierra Leone. In this study, we aimed to establish the incidence and risk factors of SSI and the related antibiotic resistance among adults attending a tertiary hospital, and a secondary health facility in Freetown, Sierra Leone. Methods This is a prospective cohort study designed to collect data from adult (18 years or older) patients who attended elective and emergency surgeries at two hospitals in Freetown between February and July, 2021. Data analysis was done using STATA version 16. Results Of 338 patients, 245 (72.5%) and 93 (27.5%) had their surgeries at the tertiary and secondary hospitals, respectively. Many were males 192 (56.8%), less than 35 years 164 (48.5%), and 39 (11.5%) developed an SSI. Of the 39 patients who acquired an SSI, 7 (17.9%) and 32 (82.1%) had their surgeries at the secondary and tertiary hospitals, respectively. The incidence of SSI is higher in contaminated 17 (43.6%) than in clean-contaminated 12 (30.8%) and clean 10 (25.6%) wounds. Wound swabs were collected in 29 (74.4%) patients, of which 18 (62.1%) had bacterial growth. In total, 49 isolates of 14 different bacteria including gram-negative 41 (83.7%) and gram-positive 8 (16.3%) isolates were identified. Of these, 32 (65.3%) were Enterobacteriaceae , 9 (18.4%) were Non-fermenting gram-negative bacilli and 10 (12.2%) were Enterococci . The most common isolates were Escherichia coli (12, 24.5%), Klebsiella pneumoniae (10, 20.4%), Acinetobacter baumannii ( 5, 10.2%), Klebsiella oxytoca (4, 8.2%) and Enterococcus faecalis (4, 8.2%). The Enterobacteriaceae were either resistance to carbapenems (4, 8.2%) or were extended-spectrum beta-lactamase (ESBL) producing organisms (29, 59.2%). Male sex [ p  = 0.031], an ASA score ≥ 2 [ p  = 0.020), administration of general anaesthesia [ p  = 0.018] and elevated fasting glucose [ p  = 0.033] were predictive of SSI. Conclusion The incidence of SSI in this study is comparable to other low- and middle-income countries, but a substantial proportion of these postoperative wounds have an ESBL-producing Enterobacteriaceae . Therefore, routine surveillance of SSI and related antibiotic resistance is required in resource-limited settings.

Objective Catheter-associated urinary tract infections (CAUTI) are common worldwide, but due to limited resources, its actual burden in low-income countries is unknown. Currently, there are gaps in knowledge about CAUTI due to lack of surveillance activities in Sierra Leone. In this prospective cohort study, we aimed to determine the incidence of CAUTI and associated antibiotic resistance in two tertiary hospitals in different regions of Sierra Leone. Results The mean age of the 459 recruited patients was 48.8 years. The majority were females (236, 51.3%). Amongst the 196 (42.6%) catheterized patients, 29 (14.8%) developed CAUTI. Bacterial growth was reported in 32 (84%) patients. Escherichia coli (14, 23.7%), Klebsiella pneumoniae (10, 17.0%), and Klebsiella oxytoca (8, 13.6%) were the most common isolates. Most isolates were ESBL-producing Enterobacteriaceae (33, 56%) and WHO Priority 1 (Critical) pathogens (38, 71%). Resistance of K. pneumoniae, K. oxytoca, E. coli , and Proteus mirabilis was higher with the third-generation cephalosporins and penicillins but lower with carbapenems, piperacillin-tazobactam and amikacin. To reduce the high incidence of CAUTI and multi-drug resistance organisms, urgent action is needed to strengthen the microbiology diagnostic services and develop and implement catheter bundles that provide clear guidance for catheter insertion, care and removal.

Introduction: Despite the prolongation of hospitalization, increase in morbidity, mortality and cost of care associated with both surgical site infections (SSIs) and antibiotic resistance, there are limited data on SSIs and antibiotic resistance to guide prevention strategies in Sierra Leone. Methods: This study assessed the burden of SSIs and related antibiotic resistance in the 34 Military Hospital (MH) and Makeni Government Hospital (MGH) located in two geographic regions of Sierra Leone using a prospective study design to collect data from adults aged 18 years or older. Results: Of the 417 patients, 233 (55.9%) were enrolled in MGH. Most were women 294 (70.5%). The incidence rate of SSI was 5.5 per 1000 patient-days, and the cumulative incidence of SSI was 8.2%. Common bacteria isolated in MH were Escherichia coli (6,33.3%) and Pseudomonas aeruginosa (3,16.7%) and in MGH were P. aeruginosa (3,42.9%) and Proteus mirabilis (2,28.9%). Of the gram-negative bacteria, 40% were Extended-spectrum beta-lactamase-producing Enterobacteriaceae, 33% were Carbapenem-resistant P. aeruginosa and 10% were carbapenem-resistant Enterobacteriaceae. Conclusion: Although the incidence of SSIs in our study is lower than previously reported, the rate of antibiotic resistance reported in this study is high. Urgent action is needed to invest in the microbiology infrastructure to support SSI surveillance and prevention strategies.

Research has proven that light treatment, specifically red light radiation, can provide more clinical benefits to human health. Our investigation was firstly conducted to characterize the tissue morphology of mouse breast post 660 nm laser radiation with low power and long-term exposure. RNA sequencing results revealed that light exposure with a higher intervention dosage could cause a number of differentially expressed genes compared with a low intervention dosage. Gene ontology analysis, protein–protein interaction network analysis, and gene set enrichment analysis results suggested that 660 nm light exposure can activate more transcription-related pathways in HC11 breast epithelial cells, and these pathways may involve modulating critical gene expression. To consider the critical role of the Wnt/T-catenin pathway in light-induced modulation, we hypothesized that this pathway might play a major role in response to 660 nm light exposure. To validate our hypothesis, we conducted qRT-PCR, immunofluorescence staining, and Western blot assays, and relative results corroborated that laser radiation could promote expression levels of β-catenin and relative phosphorylation. Significant changes in metabolites and pathway analysis revealed that 660 nm laser could affect nucleotide metabolism by regulating purine metabolism. These findings suggest that the Wnt/β-catenin pathway may be the major sensor for 660 nm laser radiation, and it may be helpful to rescue drawbacks or side effects of 660 nm light exposure through relative interventional agents.

Considering the poor effect of short text classification due to insufficient semantic information mining in the current short text matching methods, a new short text classification method is proposed based on explicit and implicit multiscale weighting semantic information interaction. First, the explicit and implicit representations of short text are obtained by a word vector model (word2vec), convolutional neural networks (CNNs), and long short-term memory (LSTM). Then, a multiscale convolutional neural network obtains the explicit and implicit multiscale weighting semantics information of short text. Finally, the multiscale weighting semantics is fused for more accurate short text classification. The experimental results show that this method is superior to the existing classical short text classification algorithms and two advanced short text classification models on the five short text classification datasets of MR, Subj, TREC, SST1 and SST2 with accuracies of 85.7%, 96.9%, 98.1%, 53.4% and 91.8%, respectively.

Aiming at the severity and distribution of abnormal behaviour, attack behaviour, compliance, and vulnerability of equipment in the power information system, this article applies big data technology to analyse and evaluate abnormal behaviour in the network, and uses machine learning to improve the network, the ability to identify security risks and the accuracy of assessments provide strong guarantees and technical support for improving the security and reliability of power information systems.

In view of the current lack of unified security authentication and control for the power Internet of Things terminal equipment, at the perception level of the power Internet of Things, the perception layer terminal access control, front-end authentication technology realization and terminal equipment abnormal behavior detection methods are proposed. This method enhances the communication security between power equipment and edge nodes, and ensures the safe and stable operation of the power Internet of Things.

With the rapid development of information and communication technology, the national electric power information system has been extensively developed, and the electric power information system is becoming more and more intelligent, complicated and networked. At the same time, the complexity of the power information system has also brought about the problem of network security, and various network threats have put forward higher requirements for the network security of the power information system. How to evaluate the cyber threat of power information system has become an important work of power information system network security. This article uses the network threat attribute indicators that the power information system faces, and combines the situation assessment method to conduct a situation assessment on the network security of the power information system.