Citations

Background/Objectives: Cesarean section (C-section) is a common surgical procedure associated with an increased risk of 30-day postpartum hospital readmissions. This study utilized machine learning (ML) to predict readmissions using a nationwide database. Methods: A retrospective analysis of the National Surgical Quality Improvement Project (2012–2022) included 54,593 patients who underwent C-sections. Random Forests (RF) and Extreme Gradient Boosting (XGBoost) models were developed and compared to logistic regression (LR) using demographic, preoperative, and perioperative data. Results: Of the cohort, 1306 (2.39%) patients were readmitted. Readmitted patients had higher rates of being of African American race (17.99% vs. 9.83%), diabetes (11.03% vs. 8.19%), and hypertension (11.49% vs. 4.68%) (p < 0.001). RF achieved the highest performance (AUC = 0.737, sensitivity = 72.03%, specificity: 61.33%), and a preoperative-only RF model achieved a sensitivity of 83.14%. Key predictors included age, BMI, operative time, white blood cell count, and hematocrit. Conclusions: ML effectively predicts C-section readmissions, supporting early identification and interventions to improve patient outcomes and reduce healthcare costs.

Campylobacter species are considered to be the leading bacterial cause of human gastroenteritis globally. Consumption of undercooked or contaminated food, such as chicken, is the main cause of human campylobacteriosis. Despite this significant zoonotic link, comparative data on virulence determinants in Campylobacter isolates across avian and human sources remain limited. This study aimed to characterize the prevalence and expression of virulence determinants in Campylobacter jejuni and Campylobacter coli isolates from chicken and human sources in Ismailia governorate, Egypt. A total of twenty C. jejuni and C. coli isolates (ten of each species) were screened for 14 virulence genes using PCR. All isolates harbored virB11, iam, racR, and tetO. Chicken isolates exhibited a significantly higher prevalence: C. jejuni (chicken): pldA, dnaJ, flaA (100%), cdtB (80%), ciaB (60%), and wlaN (0%); C. coli (chicken): pldA, dnaJ (100%), flaA (60%), cdtB (60%), ciaB (40%), and wlaN (20%). In contrast, human isolates showed a markedly lower prevalence: C. jejuni (human): dnaJ, flaA, and cdtB (20%); C. coli (human): dnaJ, flaA, and cdtB (40%). Crucially, pldA, ciaB, and wlaN were absent in all human isolates. plda and dnaJ genes showed statistically significant prevalence differences. qPCR revealed a significant upregulation (p < 0.05) of dnaJ, virB11, flaA, and iam in chicken isolates compared to human isolates, with log2 fold changes of 3.52, 2.84, 2.43, and 1.90 for C. jejuni and 3.06, 2.38, 1.51, and 1.32 for C. coli. Differential expressions of racR, cdtB, and tetO were not significant, with log2 fold changes ranging from −0.51 to 0.14. Ganglioside mimicry genes (Cst11, wlaN, Waac, ggt, and cgtB) and the carbon storage regulator gene (csrA) were absent in all human isolates. These findings underscore the significant variability in virulence gene profiles in chicken and human C. jejuni and C. coli isolates and highlight the importance of molecular characterization in the risk assessment and epidemiological surveillance of Campylobacter infections.

Importance Hidradenitis suppurativa (HS) is a chronic inflammatory skin disease with a substantial burden. Standardized global prevalence estimates and data on associated sociodemographic and risk factors are lacking. Objective To estimate the global prevalence of HS and study differences in prevalence by age, sex, geographical location, body mass index (BMI; calculated as weight in kilograms divided by height in meters squared), smoking status, gross domestic product (GDP), and Human Development Index (HDI). Data Sources and Study Selection Included studies were conducted using the standardized Global Hidradenitis Suppurativa Atlas methodology with finalized data collection before May 19, 2023. Studies were required to use a population-based sampling method and conduct clinical confirmation of HS diagnosis following a screening questionnaire. Data Extraction and Synthesis Data were independently extracted from relevant studies by 2 reviewers (D.B. and C.E.M.) using a standardized form. Extracted variables included geographic location, age, sex, BMI (median and BMI &amp;gt;30), smoking status, HS prevalence estimates (with 95% CIs), GDP, and HDI. A proportional meta-analysis using a random-effects model was conducted on the included studies. Main Outcomes and Measures The primary outcome was the point prevalence of HS, confirmed by clinical examination. Secondary outcomes included differences in HS prevalence by sex, age, BMI category, smoking status, and country-level socioeconomic indicators (GDP and HDI). All outcomes were prespecified before data analysis. Results The sample included 22 743 participants, identifying 247 patients with HS, across 25 studies in 23 countries spanning 6 continents. The median proportion of female patients with HS was 55.6%, and the median age was 34.5 years. While the prevalence estimates showed considerable inconsistency ( I 2 &amp;gt; 75%; τ 2 = 0.747), the overall random-effects global prevalence of HS was 0.99% (95% CI, 0.67%-1.46%). Female sex was the only factor observed to be associated with the prevalence estimates (β = 1.02; 95% CI, 1.01-1.03). Conclusions and Relevance In this meta-analysis, an estimated global prevalence of HS between 0.67% and 1.46% surpassed previous global estimates. Substantial global variations in HS prevalence were also observed. Female sex was the only factor associated with prevalence in this sample. Future studies assessing genetic, environmental, and etiological factors are warranted to explain the heterogeneity in prevalence.

Abstract Background One in five adults aged 40 will develop heart failure (HF) during their lifetime. Risk factors (e.g., hypertension, diabetes mellitus, coronary artery disease, etc.) lead to structural and functional changes in cardiomyocytes, resulting in HF. At the cellular level, these changes consist of mis/over‐expression of genes that regulate cardiac identity (e.g., CamK2δ, PKC, Stat3, etc.). The current paradigm for treating HF is pharmacological or device‐based intervention; however, with few exceptions, the condition worsens with time. We are proposing to implement a change in HF treatment, shifting from a drug‐centric system to a cardiac target‐specific molecular approach that would reverse hypertrophy and adverse remodelling of affected cardiomyocytes. Methods A cardiomyocyte targeting peptide (CTP) was reversibly linked to miRNA106a for delivery to a mouse model of HF. Reversal of morphological, signalling, and physiological HF parameters was measured. Additionally, CTP‐miRNA106a was introduced into a human cardiomyocyte cell line to identify mechanism(s) at play for reversing HF characteristics (e.g., hypertrophy). Results Bio‐distribution studies showed that intravenously injected CTP‐miRNA106 delivered its cargo specifically to the heart within 30 min, followed by clearance of CTP from the heart to the kidneys, and to a lesser extent, the liver by 35 h with persistence of miRNA106a in cardiomyocytes until day 7 (the latest tested time‐point). CTP‐miRNA106a reversed angiotensin2/isoproterenol‐induced hypertrophy in 90% of the treated mice. We also identified two potential HF intracellular signalling pathways/mechanisms (PLCβ1/PKC/IP3 and NF‐κB) targeted by CTP‐miRNA106a that could benefit many pathophysiologies underlying HF, including inflammation. Conclusions CTP‐miRNA106a, a first‐of‐a‐kind cardiac‐specific drug, downregulates genes involved in cardiac hypertrophy and inflammation through the PLCβ1 and CamKIIδ kinase pathways. CTP delivery of miRNA106a cargo is specific to cardiomyocytes both in vitro and in vivo, and once delivered, many HF parameters, including hypertrophy, are reversed. Key points Cardiac Targeting Peptide (CTP) delivers, specifically to the heart, reversibly linked miRNA106a. MiRNA106a targets genes in various pathways known to cause heart failure when they are over/hyper activated returning their expression to normal levels. In vivo analyses using a mouse heart failure model resulted in reversal of heart failure parameters in 19/20 mice.

We present Liquid Adaptive AI as a theoretical framework and mathematical basis for artificial intelligence systems capable of continuous structural adaptation and autonomous capability development. This work explores the conceptual boundaries of adaptive AI by formalizing three interconnected mechanisms: (1) entropy-guided hyperdimensional knowledge graphs that could autonomously restructure based on information-theoretic criteria; (2) a self-development engine using hierarchical Bayesian optimization for runtime architecture modification; and (3) a federated multi-agent framework with emergent specialization through distributed reinforcement learning. We address fundamental limitations in current AI systems through mathematically formalized processes of dynamic parameter adjustment, structural self-modification, and cross-domain knowledge synthesis, while immediate implementation faces substantial computational challenges requiring infrastructure on the scale of current large language model training facilities, we provide architectural specifications, theoretical convergence bounds, and evaluation criteria as a foundation for future research. This theoretical exploration establishes mathematical foundations for a potential new paradigm in artificial intelligence that would transition from episodic training to persistent autonomous development, offering a long-term research direction for the field. A comprehensive Supplementary Materials document provides detailed technical analysis, computational requirements, and an incremental development roadmap spanning approximately a decade.

Background: Cochlear implantation (CI) is a transformative intervention for adolescents with severe-to-profound sensorineural hearing loss, offering benefits that extend beyond auditory rehabilitation to encompass psychosocial and functional outcomes. Despite growing global evidence, there is limited data on the impact of CIs on quality of life (QoL) in low- and middle-income settings, particularly from the adolescent’s perspective. Objective: To evaluate the quality of life of adolescents with unilateral cochlear implants and to assess the influence of demographic and contextual factors on perceived QoL outcomes in a Pakistani cohort. Methods: In this cross-sectional study, 110 adolescents aged 10–19 years with at least 12 months of unilateral CI use were recruited from four rehabilitation centers in Lahore, Pakistan. Participants completed the Glasgow Benefit Inventory (GBI) and a structured demographic questionnaire. Descriptive statistics and inferential analyses compared GBI scores across gender, implant side, and educational placement, with significance set at p < 0.05. Results: The mean GBI total score was 38.6 (SD 11.4), with significant improvements observed across general, social support, and physical health domains (all p < 0.001). No statistically significant differences were found in QoL outcomes by gender, side of implantation, or school type. Strong associations were observed between general well-being and social support. Conclusion: Unilateral cochlear implantation yields substantial, multidimensional quality-of-life benefits for adolescents, independent of demographic or educational context. Integration of psychosocial support and inclusive practices is essential for maximizing post-implant outcomes.

Background: Cell-penetrating peptides cross cell membrane barriers while carrying cargoes in a functional form. Our work identified two novel lung-targeting peptides, S7A and R11A. Here, we present studies on biodistribution, the cell types targeted, and an in vitro proof of application. Methods: Studies were performed in human bronchial epithelial cells (HBECs) with and without various endocytic inhibitors, and coincubation with fluorescently labeled transferrin or endocytic markers. Cyclic R11A (cR11A) was conjugated to siRNA duplexes and anti-viral activity against SARS-CoV-2 was tested. Biodistribution studies were performed by injecting wild-type mice with fluorescently labeled peptides, and various circulation times were allowed for, as well as cross-staining of lung sections or isolated single cells with various cellular markers, followed by fluorescence-activated cell sorting or confocal microscopy. Results: cR11A showed peak uptake in 15 min, with the highest uptake in airway epithelial type II (ATII) cells, followed by p63+ basal cells and ionocytes. Cyclization increased transduction efficiencies ~100-fold. Endocytosis studies showed a decrease in peptide uptake by pre-treatment with Pitstop2 but not Amiloride or Nystatin. Endocytic marker Lamp1 showed colocalization at the earliest time point, with the escape of the peptide from endocytic vesicles later. cR11A conjugated to ant-spike and anti-envelop proteins showed anti-viral effects with an EC90 of 0.6 μM and 1.0 µM, respectively. Conclusions: We have identified a novel peptide, cR11A, that targets ATII, basal cells, and ionocytes, the cyclization of which increased transduction efficiency in vitro and in vivo. The uptake mechanism appears to be via clathrin-mediated endocytosis with escape from endocytic vesicles. cR11A can act as a vector to deliver anti-viral siRNA to epithelial cells.

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. The mean scores for the teams were calculated. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. The Dice score for the winning team was 0.65 ± 0.25. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space. The Dice scores and lesion detection rates of all algorithms diminished with decreasing tumor size, particularly for tumors smaller than 100 mm3. In conclusion, algorithms for BM segmentation require further refinement to balance high sensitivity in lesion detection with the minimization of false positives and negatives. The BraTS-METS 2023 challenge successfully curated well- annotated, diverse datasets and identified common errors, facilitating the translation of BM segmentation across varied environments and providing the tools for future development of personalized volumetric reports to patients undergoing BM treatment.