Nevin Manimala

Sci Rep. 2024 Dec 30;14(1):31585. doi: 10.1038/s41598-024-77111-4.

ABSTRACT

BAY 2413555 is a novel selective and reversible positive allosteric modulator of the type 2 muscarinic acetylcholine (M2) receptor, aimed at enhancing parasympathetic signaling and restoring cardiac autonomic balance for the treatment of heart failure (HF). This study tested the safety, tolerability and pharmacokinetics of this novel therapeutic option. REMOTE-HF was a multicenter, double-blind, randomized, placebo-controlled, phase Ib dose-titration study with two active arms. Study participants had an established diagnosis of HF with NYHA Class I-III and LVEF ≤ 45%. Patients were required to have an implanted cardiac defibrillator (ICD) or cardiac resynchronization therapy (CRT) device because of the potential for bradycardia or AV conduction delay, which may be induced by BAY 2413555. The study period included a screening and run-in period, followed by a treatment period of over 28 days, consisting of two parts, A and B, comprising 14 days each. Participants were randomized into 1 of 3 arms: a placebo arm and two BAY 2413555 arms-one receiving 1.25 mg in both Part A and Part B (BAY 1.25 mg-1.25 mg) and the other receiving 1.25 mg in Part A followed by 5 mg in Part B (BAY 1.25 mg-5 mg). The primary safety endpoint was the number of participants with treatment-emergent adverse events (TEAEs). Secondary endpoints included number of participants with high degree AV block or symptomatic pauses/ bradycardia and changes from baseline in resting heart rate after 2 and 4 weeks of dosing with BAY 2413555. Changes from baseline in heart rate recovery (HRR) at 1 and 2 min after exercise testing and chronotropic reserve (CR) were also assessed. Of the anticipated 129 participants, 22 participants were randomized: 7 to placebo, 8 to BAY 1.25 mg-1.25 mg, and 7 to BAY 1.25 mg-5 mg. The study was terminated early based on new and unexpected preclinical findings from a chronic animal toxicology study in monkeys in which evidence of increased vascular inflammation was observed, leading to a no longer favorable risk-benefit balance for the intended long-term (i.e., life-long) treatment of heart failure patients. Comparable adverse events were not encountered in REMOTE-HF. Overall, until the termination of the study, BAY 2413555 was safe and well tolerated, with no deaths or TEAEs leading to discontinuation, and no symptomatic bradycardia or AV blocks observed. There was a larger change in the mean HRR at 60 s in the pooled BAY 2413555 treatment arms in Part A (1.25 mg) compared to the placebo (+ 7.3 vs. -6.7 bpm), indicating enhanced cardiac parasympathetic activity. Administration of 1.25 mg and 5 mg BAY 2413555 was safe and well tolerated in both active treatment arms, with no concerning safety findings observed. However, due to the limited number of subjects resulting from early termination, the results should be considered with caution and viewed as exploratory. There were promising signs of target engagement, providing grounds for further exploration of the mechanism.

PMID:39738130 | DOI:10.1038/s41598-024-77111-4

Nat Commun. 2024 Dec 30;15(1):10849. doi: 10.1038/s41467-024-55146-5.

ABSTRACT

Recently, RNA velocity has driven a paradigmatic change in single-cell RNA sequencing (scRNA-seq) studies, allowing the reconstruction and prediction of directed trajectories in cell differentiation and state transitions. Most existing methods of dynamic modeling use ordinary differential equations (ODE) for individual genes without applying multivariate approaches. However, this modeling strategy inadequately captures the intrinsically stochastic nature of transcriptional dynamics governed by a cell-specific latent time across multiple genes, potentially leading to erroneous results. Here, we present SDEvelo, a generative approach to inferring RNA velocity by modeling the dynamics of unspliced and spliced RNAs via multivariate stochastic differential equations (SDE). Uniquely, SDEvelo explicitly models inherent uncertainty in transcriptional dynamics while estimating a cell-specific latent time across genes. Using both simulated and four scRNA-seq and spatial transcriptomics datasets, we show that SDEvelo can model the random dynamic patterns of mature-state cells while accurately detecting carcinogenesis. Additionally, the estimated gene-shared latent time can facilitate many downstream analyses for biological discovery. We demonstrate that SDEvelo is computationally scalable and applicable to both scRNA-seq and sequencing-based spatial transcriptomics data.

PMID:39738101 | DOI:10.1038/s41467-024-55146-5

Sci Rep. 2024 Dec 30;14(1):31617. doi: 10.1038/s41598-024-80218-3.

ABSTRACT

In this article, a nonlinear fractional bi-susceptible [Formula: see text] model is developed to mathematically study the deadly Coronavirus disease (Covid-19), employing the Atangana-Baleanu derivative in Caputo sense (ABC). A more profound comprehension of the system’s intricate dynamics using fractional-order derivative is explored as the primary focus of constructing this model. The fundamental properties such as positivity and boundedness, of an epidemic model have been proven, ensuring that the model accurately reflects the realistic behavior of disease spread within a population. The asymptotic stabilities of the dynamical system at its two main equilibrium states are determined by the essential conditions imposed on the threshold parameter. The analytical results acquired are validated and the significance of the ABC fractional derivative is highlighted by employing a recently proposed Toufik-Atangana numerical technique. A quantitative analysis of the model is conducted by adjusting vaccination and hospitalization rates using constant control techniques. It is suggested by numerical experiments that the Covid-19 pandemic elimination can be expedited by adopting both control measures with appropriate awareness. The model parameters with the highest sensitivity are identified by performing a sensitivity analysis. An optimal control problem is formulated, accompanied by the corresponding Pontryagin-type optimality conditions, aiming to ascertain the most efficient time-dependent controls for susceptible and infected individuals. The effectiveness and efficiency of optimally designed control strategies are showcased through numerical simulations conducted before and after the optimization process. These simulations illustrate the effectiveness of these control strategies in mitigating both financial expenses and infection rates. The novelty of the current study is attributed to the application of the structure-preserving Toufik-Atangana numerical scheme, utilized in a backward-in-time manner, to comprehensively analyze the optimally designed model. Overall, the study’s merit is found in its comprehensive approach to modeling, analysis, and control of the Covid-19 pandemic, incorporating advanced mathematical techniques and practical implications for disease management.

PMID:39738098 | DOI:10.1038/s41598-024-80218-3

World J Surg Oncol. 2024 Dec 30;22(1):353. doi: 10.1186/s12957-024-03647-4.

ABSTRACT

AIM: The aim of this study was to introduce the Esophagus-Sparing Anastomotic Narrowing Revision (ESANR) technique for the intraoperative management of anastomotic narrowing and to conduct a literature review to provide an algorithm for the management of narrowing and strictures that may develop secondary to esophagojejunostomy.

METHODS: Three patients with anastomotic narrowing during esophagojejunostomy were analyzed between September 2019 and June 2024. The anastomotic narrowing was detected by intraoperative gastroscopy after reconstruction. The ESANR technique was performed for the management of anastomotic narrowing. We conducted a systematic search of PubMed, Embase, and Web of Science databases for studies published up to June 2024 related to the treatment of anastomotic stricture. Data on the number of patients, sex, age, type of anastomosis, treatment, and outcomes were collected.

RESULTS: The ESANR technique proved effective for the management of anastomotic narrowing in patients who underwent esophagojejunostomy during gastric cancer surgery. No anastomotic stricture or leakage was found following ESANR, and all three patients recovered without complications. 12 studies with a total of 174 patients were analyzed. The management of anastomotic stricture, which included Balloon Dilation (BD), Endoscopic Incision Therapy (EIT), stent placement, Endoscopic combination therapy (Needle-Knife stricturotomy NKS, Balloon Dilation with Triamcinolone Injection TAC), and re-do laparoscopic esophagojejunostomy.

CONCLUSIONS: In conclusion, the ESANR technique demonstrates potential advantages in addressing anastomotic narrowing in esophagojejunostomy. However, further clinical data and analyses are necessary to verify its effectiveness and establish robust statistical support.

PMID:39736755 | DOI:10.1186/s12957-024-03647-4

BMC Vet Res. 2024 Dec 31;20(1):590. doi: 10.1186/s12917-024-04432-x.

ABSTRACT

BACKGROUND: In developing countries such as Uganda, domestic dogs suffer high burdens of infectious diseases often with high mortalities. Surveillance data on the common diseases and associated mortalities is however scanty. We thus, present results of a retrospective study of common clinical conditions and mortalities of dogs brought for treatment at the small animal clinic, Makerere University, Kampala, Uganda.

METHODS: We analysed data from the case records register of the clinic from January 2021-December, 2022. Descriptive statistics were generated using the frequency functions of R (R-4.3.3 for Windows^®). Records were reviewed for all 650 cases presented at the clinic except those presented for routine care services like vaccination and grooming.

RESULTS: Up to 51% of the dogs were female, mostly (56%) under two years old. The Alsatian (30.7%) and mongrel (22.7%) were the commonest breeds. Cases were recorded as: elective surgeries (29.2%), parvovirus infection (13.9%), skin infections (09.7%), canine babesiosis (6.9%), fractures (6.0%) and neoplasms (6%); mainly transmissible venereal tumour (TVT). Some (3.4%) dogs developed post-operative complications, while 4.8% were euthanized and 12% died during treatment. Of the dogs that died, 50% were parvovirus infection cases while other conditions included babesiosis (13%), poisoning (7.8%), pyometra (7.8%) and liver dysfunction (5.1%).

CONCLUSION: We impute that parvovirus infection and other preventable diseases were the most frequent reasons for morbidity and mortality of especially puppies in Uganda. This points to the need for epidemiologic surveillance of dog diseases and community sensitisation for improved control of dog diseases.

PMID:39736747 | DOI:10.1186/s12917-024-04432-x

J Eat Disord. 2024 Dec 30;12(1):216. doi: 10.1186/s40337-024-01174-x.

ABSTRACT

BACKGROUND: Training gaps regarding the diagnosis and management of eating disorders in diverse populations, including racial, ethnic, sexual, and gender minoritized groups, have not been thoroughly examined.

OBJECTIVE: This study aimed to examine resident physicians’ knowledge and attitudes regarding eating disorders in diverse populations, with a focus on areas for improved training and intervention.

METHODS: Ninety-two resident physicians in internal medicine, emergency medicine, obstetrics/gynecology, psychiatry, and surgery at an academic center completed an online survey from 12/1/2020-3/1/2021, which comprised multiple choice and vignette-style open-ended questions to assess knowledge and attitudes toward the management and clinical presentations of eating disorders. Overall, the survey response rate was 25.7%. Descriptive statistics were reported. Vignette-style questions were analyzed using inductive coding and the frequency of responses was reported.

RESULTS: A minority of resident physicians self-reported confidence in their knowledge of the medical complications (n = 42, 45%), risk factors (n = 38, 41%), and clinical presentations (n = 32, 35%) associated with eating disorders. Responses to vignette-style questions correctly identified relevant management methods (such as electrolyte monitoring and referral to specialty care), but demonstrated limited knowledge of the clinical presentation of eating disorders. Furthermore, most respondents reported a lack of knowledge regarding eating disorders in sexual and gender minoritized patients (n = 68, 73.9%) as well as racial and ethnic minoritized patients (n = 64, 69.6%).

CONCLUSIONS: Our findings suggest concerning gaps in knowledge and confidence among resident physicians with regard to the diagnosis and treatment of eating disorders, particularly in racial, ethnic, sexual, and gender minoritized patients. Moreover, responses to vignette-like questions indicate significant homogeneity in respondents’ perceptions of the clinical presentation of eating disorders, reflecting cultural biases which associate eating disorders with underweight, young, female patients. The majority did not feel competent in treating eating disorders in diverse populations and expressed desire for additional training in this area. More research is needed to better understand and address these gaps in eating disorder training, with the goal of increasing equity in patient outcomes.

PMID:39736744 | DOI:10.1186/s40337-024-01174-x

BMC Health Serv Res. 2024 Dec 31;24(1):1665. doi: 10.1186/s12913-024-12184-5.

ABSTRACT

BACKGROUND: The demand for quality healthcare is rising worldwide, and nurses in South Africa are under pressure to provide care with limited resources. This demanding work environment leads to burnout and exhaustion among nurses. Understanding the specific factors leading to these issues is critical for adequately supporting nurses and informing policymakers. Currently, little is known about the unique factors associated with burnout and emotional exhaustion among nurses in South Africa. Furthermore, whether these factors can be predicted using demographic data alone is unclear. Machine learning has recently been proven to solve complex problems and accurately predict outcomes in medical settings. In this study, supervised machine learning models were developed to identify the factors that most strongly predict nurses reporting feelings of burnout and experiencing emotional exhaustion.

METHODS: The PyCaret 3.3 package was used to develop classification machine learning models on 1165 collected survey responses from nurses across South Africa in medical-surgical units. The models were evaluated on their accuracy score, Area Under the Curve (AUC) score and confusion matrix performance. Additionally, the accuracy score of models using demographic data alone was compared to the full survey data models. The features with the highest predictive power were extracted from both the full survey data and demographic data models for comparison. Descriptive statistical analysis was used to analyse survey data according to the highest predictive factors.

RESULTS: The gradient booster classifier (GBC) model had the highest accuracy score for predicting both self-reported feelings of burnout (75.8%) and emotional exhaustion (76.8%) from full survey data. For demographic data alone, the accuracy score was 60.4% and 68.5%, respectively, for predicting self-reported feelings of burnout and emotional exhaustion. Fatigue was the factor with the highest predictive power for self-reported feelings of burnout and emotional exhaustion. Nursing staff’s confidence in management was the second highest predictor for feelings of burnout whereas management who listens to employees was the second highest predictor for emotional exhaustion.

CONCLUSIONS: Supervised machine learning models can accurately predict self-reported feelings of burnout or emotional exhaustion among nurses in South Africa from full survey data but not from demographic data alone. The models identified fatigue rating, confidence in management and management who listens to employees as the most important factors to address to prevent these issues among nurses in South Africa.

PMID:39736726 | DOI:10.1186/s12913-024-12184-5

BMC Nurs. 2024 Dec 30;23(1):958. doi: 10.1186/s12912-024-02629-x.

ABSTRACT

BACKGROUND: Stroke is considered one of the leading causes of both mortality and morbidity on a global scale. The significant impact on the health and quality of life of stroke survivors and their caregivers is well-acknowledged due to the stressful consequences of dependency and the need for home care. This study aims to examine the impact of online training utilizing a stroke educational program on the patient’s quality of life and their caregivers’ care burden.

MATERIALS AND METHODS: From March to August 2024, a randomized, controlled trial was conducted. In this study, a total of 60 dyads consisting of stroke patients and their caregivers participated. Participants were selected by convenient sampling method and then randomly allocated into intervention and control groups using research randomizer software. The participants in the intervention group received the educational content through the WhatsApp application during a series of fifteen sessions, each lasting between 45 and 60 min. The control group was given standard hospital education. The data collection and analysis process entailed the utilization of questionnaires, which encompassed demographics, the Stroke Specific Quality of Life Scale (SS-QOL), and the Zarit burden of care questionnaires.

RESULTS: In the intervention group, the average age of patients and caregivers was 60.23 ± 12.41 and 51.56 ± 10.42, respectively, while in the control group, it was 61.73 ± 12.61 and 53.60 ± 9.03, respectively. The intervention group demonstrated a statistically significant difference in the mean score of patient’s quality of life, comparing the baseline with the post-intervention periods (134.73 ± 33.51 vs. 90.56 ± 6.51 and 130.46 ± 30.67 vs. 90.56 ± 6.51; p < 0.05). Furthermore, a statistically significant difference in the mean score of caregiver’s care burden was noted between the baseline and post-intervention periods (80.23 ± 7.99 vs. 65.43 ± 16.52 and 80.23 ± 7.99 vs. 60.53 ± 21.34; p < 0.05).

CONCLUSION: The implementation of an online training program focused on stroke education, resulted in an improvement in the quality of life for stroke patients, as well as a reduction in the care burden for their caregivers. As a result, it is essential to provide education to patients and their caregivers to improve patient care and minimize stroke complications.

TRIAL REGISTRATION: IRCT20240609062065N1, 2024/08/31.

PMID:39736718 | DOI:10.1186/s12912-024-02629-x

J Neuroeng Rehabil. 2024 Dec 30;21(1):232. doi: 10.1186/s12984-024-01540-5.

ABSTRACT

INTRODUCTION: Neck pain affects 203 million people globally and is prevalent in various settings due to factors like poor posture, lack of exercise, and occupational hazards. Therefore, addressing ergonomic issues with solutions like a wearable robotic device is crucial. This research presents a novel assistive exosuit, characterized by its slim and lightweight structure and intuitive control without the use of hands, designed to mitigate muscle fatigue in the neck and shoulders during prolonged flexed neck posture. The efficacy of the exosuit was confirmed through human experiments and user surveys.

METHODS: The preliminary feasibility experiment was conducted with five subjects for 15 min to verify the effect of supporting the weight of the head with a wire on reducing neck muscle fatigue. The prime experiment was conducted with 26 subjects for 15 min to quantitatively evaluate the reduction in muscle fatigue achieved by wearing the exosuit and to assess its qualitative usability from the user’s perspective. For all experiments, surface electromyography (sEMG) data was measured from upper trapezius (UT) and splenius capitis (SC) muscles, the two representative superficial muscles responsible for sustaining flexed neck posture. The analysis of the device’s efficiency utilized two parameters: the normalized root mean square value (nRMS), which was employed to assess muscle activity, and the normalized median frequency (nMDF), which was utilized to gauge the extent of muscle fatigue. These parameters were statistically analyzed with the IBM SPSS statistic program.

RESULTS: When wearing the exosuit, the nMDF of UT and SC increased by 7.18% (p < 0.05) and 5.38% (p < 0.05), respectively. For the nRMS, no significant differences were observed in either muscle. The nMDF slope of UT and SC increased by 0.63%/min (p < 0.01) and 0.34%/min (no significance). In the context of the nRMS slope, UT exhibited a reduction of 0.021% MVC/min (p < 0.05), while SC did not demonstrate any statistically significant outcomes. The exosuit received an average system usability scale score of 66.83.

CONCLUSIONS: Based on both qualitative and quantitative evaluations, our proposed assistive exosuit demonstrated that it promises the significant reduction of muscle fatigue in the neck and shoulders.

PMID:39736717 | DOI:10.1186/s12984-024-01540-5

World J Surg Oncol. 2024 Dec 31;22(1):354. doi: 10.1186/s12957-024-03649-2.

ABSTRACT

OBJECTIVE: Postoperative venous thromboembolism (VTE) is a potentially life-threatening complication. This study aimed to develop a predictive model to identify independent risk factors and estimate the likelihood of VTE in patients undergoing surgery for cervical cancer.

METHODS: We conducted a retrospective cohort study involving 1,174 patients who underwent surgery for cervical carcinoma between 2019 and 2022. The cohort was randomly divided into training and validation sets at 7:3. Univariate and multivariate logistic regression analyses were used to determine the independent factors associated with VTE. The results of the multivariate logistic regression were used to construct a nomogram. The nomogram’s performance was assessed via the concordance index (C-index) and calibration curve. Additionally, its clinical utility was assessed through decision curve analysis (DCA).

RESULTS: The predictive nomogram model included factors such as age, pathology type, FIGO stage, history of chemotherapy, the neutrophil-lymphocyte ratio (NLR), fibrinogen degradation products (FDP), and D-dimer levels. The model demonstrated robust discriminative power, achieving a C-index of 0.854 (95% CI: 0.799-0.909) in the training cohort and 0.757 (95% CI: 0.657-0.857) in the validation cohort. Furthermore, the nomogram showed excellent calibration and clinical utility, as evidenced by the calibration curve and decision curve analysis (DCA) results.

CONCLUSIONS: We developed a high-performance nomogram that accurately predicts the risk of VTE in cervical cancer patients undergoing surgery, providing valuable guidance for thromboprophylaxis decision-making.

PMID:39736708 | DOI:10.1186/s12957-024-03649-2