Browse Articles

Fluvoxamine and lycopene alleviate cisplatin-induced kidney fibrosis by modulating miR-21 and fibrotic signaling pathways

Abstract Cisplatin (Cis) is a widely utilized chemotherapy drug for managing various cancers, but its efficacy is limited by nephrotoxicity, which frequently results in kidney fibrosis. Oxidative stress, inflammation, and the fibrotic transforming growth factor beta/small mothers against decapentaplegic (TGF-β/SMAD) pathway are among the underlying mechanisms. Long-term exposure to Cis causes apoptosis, extracellular matrix deposition, and fibrosis. Fluvoxamine (FLV), a selective serotonin reuptake inhibitor and sigma‑1 receptor agonist, lycopene (LYC), a carotenoid antioxidant, and their combination showed a novel protective influence on kidney fibrosis in male albino rats induced by administering Cis (7 mg/kg) by intraperitoneal (IP) injection once weekly for 4 weeks. FLV (5 mg/kg), LYC (10 mg/kg), and their combination were administered orally (PO) once daily for four weeks. Treatment reduced blood urea nitrogen (BUN), serum creatinine (S.cr), nuclear factor kappa B (NF-κB), microRNA-21 (miR-21) levels, and the pro-apoptotic response reflected by decreased Bcl-2-associated X protein (Bax) immunoreactivity while enhancing antioxidant activity. They also attenuated fibrosis markers, including TGF-β1, SMAD3, collagen I (Col-I), and alpha-smooth muscle actin (α-SMA). FLV and LYC effectively attenuated Cis-induced kidney fibrosis through antioxidant, antiapoptotic, and antifibrotic mechanisms. These findings may provide a new therapeutic approach to counter Cis-induced nephrotoxicity in clinical settings.

Electrolyte imbalance in Asphyxiated term neonates: Incidence, predictors, and outcomes from a prospective cohort study in Northern Uganda

Background Birth asphyxia is a major cause of neonatal morbidity and mortality, particularly in low-resource settings. Hypoxia and metabolic derangements that occur during asphyxia predispose neonates to electrolyte abnormalities, which may worsen the clinical course and contribute to poor outcomes. Early recognition and management of such imbalances can improve survival and prevent long-term neurological damage. This study aimed to determine the incidence, predictors, and early outcomes of electrolyte imbalance among term neonates admitted with birth asphyxia in Lira Regional Referral Hospital, Uganda. Methods A hospital-based prospective cohort study was conducted among term neonates admitted with birth asphyxia, defined as a 5-minute Apgar score <7. Serum sodium, potassium, and calcium levels were measured at admission, and repeated on days 3, 7, and 14 for those still admitted. Clinical information including maternal and perinatal characteristics was recorded. Modified Poisson regression (using SPSS) was performed to identify independent predictors of electrolyte imbalance. Early outcomes, including mortality and length of hospital stay, were documented within the first 14 days. Results A total of 152 neonates were enrolled; 52.6% were male. During follow-up, 42 (29.0%) developed hyponatremia, 29 (19.5%) hyperkalemia, and 31 (21.2%) hypocalcemia. Independent predictors of hyponatremia included low Apgar score (0–3), severe hypoxic ischemic encephalopathy (HIE), prolonged intravenous fluid administration (>48 hours), resuscitation at birth, and dehydration. Convulsions, prolonged intravenous fluids, and resuscitation were significantly associated with hypocalcemia, while hyperkalemia was linked to low birth weight and prolonged intravenous fluids (p < 0.05 for all). Mortality was highest among neonates with hyperkalemia, whereas hypocalcemia was significantly associated with prolonged hospital stay (p < 0.05). Conclusion Electrolyte imbalances are common among term neonates with birth asphyxia and hypoxic-ischemic encephalopathy, with hyponatremia, hypocalcemia, and hyperkalemia occurring in nearly one-fifth to one-third of cases. These imbalances are predicted by HIE severity, convulsions, prolonged intravenous fluid therapy, and are associated with increased early neonatal mortality, highlighting the need for routine electrolyte monitoring, standardized intravenous fluid protocols, and timely correction of derangements to improve outcomes.

See the clouds streaming and vanishing around this planet — 690 light years away

Comparison of early versus late tracheostomy in trauma patients: a multicenter propensity score-matched cohort study

Tuberculosis in households with infectious cases in Kampala city: Harnessing health data science for new insights on an ancient disease with persistent, unresolved problems

Tuberculosis (TB) is prevalent in Uganda and overlaps with a high rate of HIV/TB coinfection. While nearly all hospital-based TB cases in Kampala, the capital of Uganda, show clear TB symptoms, 30% or more of undiagnosed TB cases found through active screening are asymptomatic. Additionally, the host risk factors for TB in Kampala cannot be distinguished from environmental risk factors. These TB-specific challenges are just part of the complexity, especially in areas with high HIV/AIDS burden. Data science techniques, especially Artificial Intelligence (AI) and Machine Learning (ML) algorithms, could help untangle this complexity by identifying factors related to the host, pathogen, and environment, which are difficult to explain or predict with traditional/conventional methods. In this project, we will use health data science approaches (AI/ML) to identify factors driving TB transmission within households and reasons for anti-TB treatment failure. We will utilize the computational resources at Makerere University and available demographic, clinical, and laboratory data from TB patients and their contacts to develop AI and ML algorithms. These will aim to: (1) identify patients at baseline (month 0) unlikely to convert their sputum or culture results by months 2 and 5, thus at risk of failing TB treatment; (2) identify household contacts of TB cases who are at risk of developing TB disease, as well as contacts who may resist TB infection despite repeated exposure to M. tuberculosis . Achieving these objectives will provide evidence that data science methods are effective for early detection of potential TB cases and high-risk patients, thereby helping to reduce TB transmission in the community. The study protocol received approval from the School of Biomedical Sciences IRB, protocol number SBS-2023-495.

Predictors of deep brain stimulation response in patients with obsessive compulsive disorder: a systematic review and meta-analysis

Abstract Obsessive-compulsive disorder (OCD) is a chronic and debilitating condition, often resistant to conventional treatments. Deep brain stimulation (DBS) emerges as a promising intervention, but its efficacy varies among patients, underscoring the need to understand the predictive factors influencing its outcomes. To evaluate and compare disease and patient characteristics in their ability to predict response to DBS in OCD patients. All major databases were searched for original studies. This study differentiated responders, partial responders, and non-responders based on relative post-treatment Y-BOCS score changes and pre-defined Y-BOCS cut-off scores. Clinical predictors were assessed using qualitative synthesis, univariate analysis, stepwise and regularisation-tuned multivariate linear and logistic regression analyses. The meta-analysis, comprising 28 studies with a pooled sample of 296 patients, found that higher baseline Y-BOCS scores significantly predicted favourable long-term DBS response in the multivariate regression analysis ( p  = 0.0075), whereas antipsychotic use was identified as a significant predictor of non-response ( p  = 0.0138). In the univariate analysis, antidepressant use was negatively associated with DBS response ( p  = 0.027), while anxiolytic use was positively associated with short-term improvement ( p  = 0.025). Symmetry, hoarding, and perfectionism symptomology at baseline predicted reduced short-term improvement ( p  = 0.027). Adjusting for stimulation target, aggression, and intrusive thoughts, baseline symptomology emerged as a positive predictor of DBS response in the multivariate model ( p  = 0.0475). In the multivariate analysis, excluding studies with high risk of bias, the symptom combination anxiety, avoidance, and fear was associated with DBS response ( p  = 0.024). Disease duration, gender, anatomical target location, and age did not predict DBS response ( p  > 0.05). This study found that a higher baseline severity of OCD symptoms significantly predicted a greater likelihood of long-term response to DBS. In contrast, the use of antipsychotics and antidepressants was associated with poorer outcomes, while anxiolytic use appeared to support short-term improvement. Compulsive symptom profiles characterised by symmetry, hoarding, and perfectionism were associated with non-response, whereas the presence of aggression and intrusive thoughts, as well as anxiety, fear and avoidance symptomology, was linked to a positive response. Further research with homogenous methodology and outcome reporting, as well as randomised control trials, are required to further elucidate these phenotypes and allow for more personalised and, thus, likely more effective DBS treatment strategies for all OCD patients.

Immunomodulatory effects of a multi-component pharmacological intervention on diabetic peripheral neuropathy in type 2 diabetic rats: An exploratory study

Background Diabetic peripheral neuropathy (DPN) is a common complication of type 2 diabetes mellitus (T2DM) and is closely linked to immune and inflammatory dysregulation. Multi-component pharmacological interventions have been explored as complementary approaches for metabolic and immune modulation; however, their effects on DPN and related mechanisms remain incompletely understood. Methods A rat model of T2DM-associated peripheral neuropathy was established, and a multi-component pharmacological intervention (MPCI) was administered for 8 weeks. Peripheral nerve dysfunction was evaluated by motor and sensory nerve conduction velocities (MNCV and SNCV), behavioral outcomes, and histological/ultrastructural assessments. In parallel, spleen tissues were collected for transcriptomic profiling. RNA sequencing was performed to identify differentially expressed genes and immune-related pathways, and representative molecules involved in inflammatory regulation were further validated using western blotting and quantitative real-time PCR in sciatic nerve tissue. Results MPCI administration significantly ameliorated peripheral nerve dysfunction in T2DM rats, as evidenced by improved nerve conduction velocities and pathological features. Transcriptomic analysis of spleen tissue revealed that MPCI was associated with broad remodeling of diabetes-related immune and inflammatory gene programs. In parallel, sciatic nerve analyses showed attenuation of NF-κB/c-Jun–associated inflammatory signaling and modulation of inhibitory regulators at both the protein and mRNA levels. Conclusion These findings indicate that MPCI improves T2DM-associated DPN and is associated with splenic immune remodeling and attenuation of peripheral nerve inflammatory signaling, providing exploratory evidence for associations between splenic immune transcriptomic remodeling and peripheral nerve inflammatory signaling.

Cymbopogon proximus essential oil promotes wound healing and attenuates inflammation through mechanisms supported by network pharmacology and molecular docking

Abstract Cymbopogon proximus Chiov. (Poaceae), widely used in traditional medicine for inflammatory conditions, was investigated for its anti-inflammatory and wound-healing pharmacological potential through an integrated phytochemical, biological, and in silico approach. Essential oils from Cymbopogon proximus and Cymbopogon citratus were isolated by hydrodistillation and chemically characterized by gas chromatography–mass spectrometry coupled with chemometric discrimination. Biological screening first assessed safety and anti-inflammatory activity in vitro using cytotoxicity evaluation, human red blood cell membrane stabilization, and modulation of inflammatory gene expression in lipopolysaccharide-stimulated white blood cells. Based on the superior in vitro profile, C. proximus essential oil was advanced to in vivo pharmacological evaluation in an excisional wound model, with assessment of wound contraction, histopathology, and inflammatory biomarkers. C. proximus exhibited a distinct piperitone-dominant volatile profile and demonstrated stronger anti-inflammatory activity than C. citratus , evidenced by membrane-stabilizing effects and downregulation of key pro-inflammatory mediators, particularly tumor necrosis factor alpha, within non-cytotoxic concentrations. Topical administration accelerated wound closure, improved re-epithelialization, and reduced inflammatory cell infiltration, with activity comparable to a reference topical preparation. Network pharmacology and molecular docking further supported a multi-target mechanism involving inflammation- and repair-related signaling nodes. Collectively, these findings identify C. proximus essential oil as a promising multi-component anti-inflammatory agent with therapeutic potential in inflammation-driven wound repair.

Estimation of battery SOC using a combined approach of temporal convolutional networks and Unscented Kalman Filter

In recent years, power batteries have been widely used in electric vehicles, and the evaluation of state of charge (SOC) is an important parameter in battery management systems. Therefore, in this paper, we propose a time-series convolutional network that employs extended convolution and residual concatenation to efficiently process time-series data with parallelism and flexibility, and combines it with Unscented Kalman Filter (UKF) to further improve the accuracy and reduce the output fluctuation, so as to estimate the state of charge of lithium-ion batteries. We conducted experiments using the University of Maryland’s Dynamic Stress Test (DST), US06 test, and Federal Urban Driving Scheme (FUDS) datasets, and compared the proposed method with Convolutional Neural Networks (CNNs), Long and Short-Term Memory Networks (LSTMs), and Gated Recurrent Units (GRUs). Experimental results demonstrate that the proposed framework achieves superior estimation accuracy and robustness. Specifically, the proposed method achieves mean absolute error (MAE) values of 1.305%, 1.470%, and 1.015% under the DST, US06, and FUDS conditions, respectively, with an average Root Mean Square Error (RMSE) of 1.566% and a MAE below 1.263%. Compared with existing deep learning models, the proposed method reduces the SOC estimation error by approximately 7.6%–39.6% under different driving conditions. These results verify the effectiveness and robustness of the proposed hybrid SOC estimation framework.

Template-driven scaffolding of SCFFBXO42 regulates PP2A degradation

Optimal hidden layer size for Levenberg–Marquardt based neural network for SoC estimation under different temperature conditions

Perceptions, attitudes, behaviours and barriers towards obesity among people with obesity and health care professionals in Indonesia: An exploratory online survey

Background The Awareness, Care, and Treatment In Obesity maNagement Asia Pacific (ACTION APAC) online survey identified perceptions, attitudes, and barriers to effective obesity care among People with Obesity (PwO) and Health Care Professionals (HCP) across nine APAC countries. Here, we present findings from Indonesia. Methods This was a cross-sectional, observational, descriptive survey in PwO (≥18-year-old) with self-reported body mass index of ≥25 kg/m 2 and HCPs who spent ≥50% of their time in direct patient care. The survey was conducted between 20 April, 2022 to 11 May, 2022. The questionnaires were approved by the institutional review board as per local regulations. Findings A total of 1,000 PwO and 200 HCPs completed the survey. Notable differences were observed among PwO and HCPs in acknowledging obesity as a chronic disease (54% PwO and 90% HCPs), considering weight loss as PwO responsibility (91% PwO and 17% HCPs) and in agreeing that PwO were motivated to lose weight (76% PwO and 50% HCPs). Almost, two-thirds (67%) of PwO perceived themselves as either overweight or normal weight while only 30% discussed weight with their HCPs in the past five years. Financial concerns (45%) and assuming self-responsibility for weight loss (43%) were cited as the top reasons for not discussing. Only 53% HCPs initiated weight conversations, as they believed that PwO were either not motivated (55%) or not able to lose weight (51%). When discussed, most (68%) HCPs recommended lifestyle changes. Interpretation Our study identified gaps in understanding obesity as a disease and its management among PwO and HCPs, highlighting a need for increased awareness to improve obesity care in Indonesia.

Ebola outbreak spirals out of control: how might it have started?

Hybrid deep learning approach for early emphysema diagnosis combining fuzzy C-means, TransUNet, and faster mask R-CNN

Abstract Emphysema is a chronic condition of the lungs and it has to be diagnosed at an early age as it can harm the lungs effectively as they can be treated and controlled. The quality of the diagnosis depends on the efficient detection of the emphysematous areas in CT scans, yet the majority of the current methods fail in terms of noise, slight tissue differences, and delimiting the area composition. In this work, a new Hybrid IFCM-TransUNet-Faster Mask R-CNN framework to detect and localize prominently early emphysema cases will be proposed, featuring Improved Fuzzy C-Means (IFCM) preliminarily segmentation using noise-resistant Improved Fuzzy C-Means (IFCM) clustering, TransUNet preliminary feature extraction with transformers, and instance detection and localization provided by Faster Mask R-CNN. Such a combination is unique to offer pixel-level segmentation and object-level detection, as well as being more accurate and interpretable than single deep models. On 2000 CT images, the experiments reached a validation accuracy of 97.4 and a Dice coefficient of 0.97 and surpassed baselines on U-Net, Mask R-CNN, and DeepLabV3 + . The suggested system improves the minimization of false positives, boundary delineation improvement, and automated and understandable emphysema detection, which can pass clinical decision support.

Balloon pressure monitoring for radial artery hemostasis after transradial coronary procedures: protocol for a randomized controlled trial

Background Forearm radial artery occlusion (RAO) is a common complication after transradial coronary procedures. Traditional patent hemostasis, relying on operator-dependent assessment, results in labor-intensive processes and inconsistent RAO rates. Methods This is a single-center, prospective, randomized, open-label, parallel-group superiority trial. We plan to enroll 818 patients scheduled for transradial coronary angiography. Participants will be randomly assigned (1:1) to either a novel balloon pressure monitoring system (integrating high-precision digital manometry with physiologically-phased decompression) or traditional patent hemostasis. The primary outcome is the incidence of ultrasound-confirmed forearm RAO at 24 hours post-procedure. Key secondary outcomes include rates of access-site vascular complications and bleeding events, as well as objective metrics of hemostasis efficiency. Recruitment Status: Recruitment commenced in September 2024 and is ongoing; the target sample size is anticipated to be reached by May 2026. Analysis will follow the intention-to-treat principle. Results/ Trial Status As a protocol paper, no results are reported. The trial is currently in the recruitment phase. Conclusions This trial will provide the first large-scale randomized evidence on whether digital manometry-guided compression reduces RAO, potentially bridging the efficacy-effectiveness gap between optimized research protocols and routine practice. Trial registration The trial was registered with the Chinese Clinical Trial Registry (ChiCTR) in August 2024, under the registration number ChiCTR2400088258.

Kainic acid pig model of hippocampal epilepsy

Comparison between enzyme-linked immunospot assay and intracellular cytokine flow cytometry assay of cytomegalovirus-specific T-cell response in healthy participants

Human cytomegalovirus (HCMV) usually establishes a lifelong latent infection after primary infection. Reactivation occurs sporadically and is controlled by cell-mediated immune response (CMIR). Monitoring of CMIR against CMV is mandatory in immunocompromised patients to adjust immunosuppressive drugs to prevent serious end-organ damage after CMV reactivation. Intracellular staining (ICS) and enzyme-linked immunospot (ELISPOT) quantifying CMV-specific T-cells are generally used as surrogate markers for CMIR against CMV. Whether the results of these 2 methods correlate well is not known. This study compared the numbers of CMV-specific T-cells identified by ELISPOT and ICS in healthy adult volunteers. Correlation with CMV serological status was explored. Thirty peripheral blood samples from healthy individuals were quantified for IFN-γ-producing cells after stimulation with whole CMV and IE1 using ICS, and IFN-γ-secreting cells after stimulation with whole CMV lysate and IE1 peptide pool using ICS and IFN-γ-secreting cells by ELISPOT. Anti-CMV IgG levels were analyzed concomitantly using a chemiluminescent microparticle immunoassay. There were 30 healthy participants, 15 (50%) male, with a mean age of 37.8 (± 7.6) years. Twenty-eight (93.3%) were seropositive against CMV. The CMV-specific CD3 + cells, as measured by ICS, were highly correlated with the spot numbers obtained by the ELISPOT, irrespective of CMV antigens used (whole CMV, r  =  0.7677, p  < 0.0001; IE1, r  =  0.6516, p  < 0.0001). The numbers of CMV-specific CD3 + cells quantified by IE1 stimulation by these 2 assays were statistically correlated with anti-CMV IgG levels (ICS, r  =  0.5070, p  = 0.004; ELISPOT, r = 0.4384, p  = 0.015). Among the 30 participants, CMV-specific T cells were detected in all participants (100%), including the two seronegative individuals. The present study demonstrated that CMV-specific T-cells measured by ICS and ELISPOT assays were well correlated, suggesting that these assays could be used to monitor CMV-specific T-cells. CMV IgG levels may reflect prior CMV infection and CMV-specific cell-mediated immunity (CMIR) in immunocompetent individuals.

Torsional strength and deformation characteristics of HSC deep beams reinforced with cross-inclined stirrups

Abstract The findings of this paper provide essential knowledge for the effective and economical design of deep beams subjected to pure torsion. As a result, this paper studies the torsional behavior of nine high-strength concrete (HSC) deep beams reinforced by cross-inclined stirrups. The test parameters comprised stirrup type (conventional stirrups and cross-inclined stirrups), inclination angle (45 and 60 degrees), stirrup spacing (100, 150, and 200 mm), the main longitudinal bars (4∅12 and 4∅18), and the side longitudinal bars (without and 4∅12). Torsional moment capacity, crack pattern, twisting angle, strain on longitudinal and transverse reinforcement, and torsional deformation characteristics in terms of stiffness and ductility were used to evaluate the tested beams’ torsional response. Test results clearly indicated that using the cross-inclined stirrup reinforcement improved the evaluated deep beams with conventional stirrups’ torsional capacity, stiffness, ductility, and overall performance. Reducing stirrup spacing from 200 to 100 mm increased torsional capacity by 49% for conventional stirrups and 65% for cross-inclined stirrups. An ABAQUS three-dimensional finite element model was built to evaluate the model’s ability to replicate the experimental beams’ torsional behavior. A theoretical model for ultimate torque calculation was also created. Compared to experimental findings, numerical and theoretical investigations were satisfactory.

Catheter body-surface fixation after transurethral prostate resection: A low-value nursing practice as evidenced in a randomized controlled trial

This randomized controlled trial is aimed at evaluating whether external fixation of the urinary catheter to the body surface represents a low-value nursing intervention for patients undergoing transurethral resection of the prostate (TURP). A total of 208 patients who received indwelling urinary catheters after TURP in a tertiary hospital in Qingdao, China between June 2024 and May 2025 were randomly assigned to one of two groups: a nonexternal fixation group (n = 103) and an external body surface fixation group (n = 105). A between-group comparison of outcomes included postoperative hematuria, incidence of catheter-associated urinary tract infection (CAUTI), unplanned catheter removal, occurrence of urinary catheter-related meatal pressure injury (UCR-MPI), and associated economic costs. No significant differences were observed between the two groups in terms of postoperative hematuria or CAUTI incidence (P > 0.05). Unplanned catheter removal did not occur in either group. However, UCR-MPI occurred significantly more frequently in the external fixation group (9 patients) than it did in the nonexternal fixation group (1 patient) (P < 0.05). Additionally, the external fixation group incurred higher costs for personnel and consumables. External fixation of the urinary catheter to the body surface after TURP is associated with increased economic costs, reduced patient comfort, and a higher incidence of UCR-MPI, which indicates that it constitutes a low-value nursing practice. Nonexternal fixation appears to be a safe and effective alternative for post-TURP patients undergoing early mobilization.

Deep learning enables automated detection of dinosaur footprints with high accuracy

Abstract Dinosaur footprints provide crucial paleoecological information about locomotion, behavior, and tracksite distributions. Traditional tracksite surveys rely on time-consuming manual identification by expert researchers, with results influenced by preservation quality and subjective interpretation. Here, we present an automated detection system for dinosaur footprints using You Only Look Once version 8 (YOLOv8), an effective object detection neural network. We trained the model on 49,242 images from the AI-Hub dataset, comprising theropod, ornithopod, and sauropod footprints from Korean tracksites. The final model achieved a mean average precision (mAP50) of 0.949 and mAP50-95 of 0.660. To address resolution-dependent detection challenges, we developed a multi-scale detection approach combining predictions across eight different image resolutions. Testing on previously unseen tracksites demonstrated successful detection of footprints from both Korea and Brazil, including the ichnospecies Farlowichnus rapidus , indicating strong cross-regional generalization. Our results show that detection accuracy depends on illumination conditions, preservation quality, and the presence of outline markings. This deep learning approach offers an efficient alternative to manual tracksite exploration, enabling rapid spatial distribution mapping of extensive dinosaur tracksites while providing objective, reproducible detection results.