Identification of Potential Factors Associated with Cellulitis Following Lymphovenous Bypass Surgery in Breast Cancer Survivors.

BACKGROUND: Breast cancer is one of the most common types of cancer, with around 2.3 million cases diagnosed in 2020. One in five cancer patients develops chronic lymphedema caused by multifactorial triggers and treatment-related factors. This can lead to swelling, skin infections, and limb dysfunction, negatively affecting the patient's quality of life. This retrospective cohort study aimed to determine the associations between demographic and breast cancer characteristics and postoperative cellulitis in breast cancer survivors who underwent lymphovenous bypass surgery (LVB) at Mayo Clinic, Florida. METHODS: We performed a retrospective chart review. Data were collected retrospectively from 2016 to 2022. Sixty adult breast cancer survivors who underwent LVB were included in the final analysis based on specific inclusion and exclusion criteria. Patients were excluded if they did not meet the inclusion criteria or had incomplete follow-up data. Demographic and surgical data were extracted, including body mass index (BMI), type of anastomosis, number of anastomoses, and preoperative cellulitis status. Lymphedema measurements were performed using tape measurements. Fisher's exact test was used to determine statistically significant associations between variables and postoperative cellulitis. RESULTS: Postoperative cellulitis was more common in patients aged 60 to 69 years (43.2%), whites (75.0%), overweight or obese (90.9%), with one to four anastomoses (81.8%), and nonsmokers (79.5%). The mean International Society of Lymphology (ISL) criteria for both postoperative cellulitis and no postoperative cellulitis was 1.93. Statistically significant associations with postoperative cellulitis were found for the number of anastomoses (p = 0.021), smoking status (p = 0.049), preoperative cellulitis (p = 0.04), and the length of years with lymphedema diagnosis variable (p = 0.004). CONCLUSION: Our results suggest that a greater number of anastomoses, smoking, preoperative cellulitis, and years with lymphedema are significantly associated with an increased risk of postoperative cellulitis. Awareness of these risk factors is crucial for monitoring and early treatment of infections following surgery.

Evaluating Large Language Model (LLM) Performance on Established Breast Classification Systems.

Medical researchers are increasingly utilizing advanced LLMs like ChatGPT-4 and Gemini to enhance diagnostic processes in the medical field. This research focuses on their ability to comprehend and apply complex medical classification systems for breast conditions, which can significantly aid plastic surgeons in making informed decisions for diagnosis and treatment, ultimately leading to improved patient outcomes. Fifty clinical scenarios were created to evaluate the classification accuracy of each LLM across five established breast-related classification systems. Scores from 0 to 2 were assigned to LLM responses to denote incorrect, partially correct, or completely correct classifications. Descriptive statistics were employed to compare the performances of ChatGPT-4 and Gemini. Gemini exhibited superior overall performance, achieving 98% accuracy compared to ChatGPT-4's 71%. While both models performed well in the Baker classification for capsular contracture and UTSW classification for gynecomastia, Gemini consistently outperformed ChatGPT-4 in other systems, such as the Fischer Grade Classification for gender-affirming mastectomy, Kajava Classification for ectopic breast tissue, and Regnault Classification for breast ptosis. With further development, integrating LLMs into plastic surgery practice will likely enhance diagnostic support and decision making.

EMBER creates a unified space for independent breast cancer transcriptomic datasets enabling precision oncology.

Transcriptomics has revolutionized biomedical research and refined breast cancer subtyping and diagnostics. However, wider use in clinical practice is hampered for a number of reasons including the application of transcriptomic signatures as single sample predictors. Here, we present an embedding approach called EMBER that creates a unified space of 11,000 breast cancer transcriptomes and predicts phenotypes of transcriptomic profiles on a single sample basis. EMBER accurately captures the five molecular subtypes. Key biological pathways, such as estrogen receptor signaling, cell proliferation, DNA repair, and epithelial-mesenchymal transition determine sample position in the space. We validate EMBER in four independent patient cohorts and show with samples from the window trial, POETIC, that it captures clinical responses to endocrine therapy and identifies increased androgen receptor signaling and decreased TGFß signaling as potential mechanisms underlying intrinsic therapy resistance. Of direct clinical importance, we show that the EMBER-based estrogen receptor (ER) signaling score is superior to the immunohistochemistry (IHC) based ER index used in current clinical practice to select patients for endocrine therapy. As such, EMBER provides a calibration and reference tool that paves the way for using RNA-seq as a standard diagnostic and predictive tool for ER+ breast cancer.

Artificial Intelligence in Postoperative Care: Assessing Large Language Models for Patient Recommendations in Plastic Surgery.

Since their release, the medical community has been actively exploring large language models' (LLMs) capabilities, which show promise in providing accurate medical knowledge. One potential application is as a patient resource. This study analyzes and compares the ability of the currently available LLMs, ChatGPT-3.5, GPT-4, and Gemini, to provide postoperative care recommendations to plastic surgery patients. We presented each model with 32 questions addressing common patient concerns after surgical cosmetic procedures and evaluated the medical accuracy, readability, understandability, and actionability of the models' responses. The three LLMs provided equally accurate information, with GPT-3.5 averaging the highest on the Likert scale (LS) (4.18 ± 0.93) (p = 0.849), while Gemini provided significantly more readable (p = 0.001) and understandable responses (p = 0.014; p = 0.001). There was no difference in the actionability of the models' responses (p = 0.830). Although LLMs have shown their potential as adjunctive tools in postoperative patient care, further refinement and research are imperative to enable their evolution into comprehensive standalone resources.

Comparative Analysis of Large Language Models in Emergency Plastic Surgery Decision-Making: The Role of Physical Exam Data.

In the U.S., diagnostic errors are common across various healthcare settings due to factors like complex procedures and multiple healthcare providers, often exacerbated by inadequate initial evaluations. This study explores the role of Large Language Models (LLMs), specifically OpenAI's ChatGPT-4 and Google Gemini, in improving emergency decision-making in plastic and reconstructive surgery by evaluating their effectiveness both with and without physical examination data. Thirty medical vignettes covering emergency conditions such as fractures and nerve injuries were used to assess the diagnostic and management responses of the models. These responses were evaluated by medical professionals against established clinical guidelines, using statistical analyses including the Wilcoxon rank-sum test. Results showed that ChatGPT-4 consistently outperformed Gemini in both diagnosis and management, irrespective of the presence of physical examination data, though no significant differences were noted within each model's performance across different data scenarios. Conclusively, while ChatGPT-4 demonstrates superior accuracy and management capabilities, the addition of physical examination data, though enhancing response detail, did not significantly surpass traditional medical resources. This underscores the utility of AI in supporting clinical decision-making, particularly in scenarios with limited data, suggesting its role as a complement to, rather than a replacement for, comprehensive clinical evaluation and expertise.

Serotype and genomic diversity of dengue virus during the 2023 outbreak in Pakistan reveals the circulation of genotype III of DENV-1 and cosmopolitan genotype of DENV-2.

Dengue, a mosquito-borne viral disease, poses a significant public health challenge in Pakistan, with a significant outbreak in 2023, prompting our investigation into the serotype and genomic diversity of the dengue virus (DENV). NS-1 positive blood samples from 153 patients were referred to the National Institute of Health, Pakistan, between July and October 2023. Among these, 98 (64.1%) tested positive using multiplex real-time PCR, with higher prevalence among males (65.8%) and individuals aged 31-40. Serotyping revealed DENV-1 as the predominant serotype (84.7%), followed by DENV-2 (15.3%). Whole-genome sequencing of 18 samples (DENV-1 = 17, DENV-2 = 01) showed that DENV-1 (genotype III) samples were closely related (>99%) to Pakistan outbreak samples (2022), and approx. > 98% with USA (2022), Singapore and China (2016), Bangladesh (2017), and Pakistan (2019). The DENV-2 sequence (cosmopolitan genotype; clade IVA) shared genetic similarity with Pakistan outbreak sequences (2022), approx. > 99% with China and Singapore (2018-2019) and showed divergence from Pakistan sequences (2008-2013). No coinfection with dengue serotypes or other viruses were observed. Comparisons with previous DENV-1 sequences highlighted genetic variations affecting viral replication efficiency (NS2B:K55R) and infectivity (E:M272T). These findings contribute to dengue epidemiology understanding and underscore the importance of ongoing genomic surveillance for future outbreak responses in Pakistan.

Large Language Models for Intraoperative Decision Support in Plastic Surgery: A Comparison between ChatGPT-4 and Gemini.

Background and Objectives: Large language models (LLMs) are emerging as valuable tools in plastic surgery, potentially reducing surgeons' cognitive loads and improving patients' outcomes. This study aimed to assess and compare the current state of the two most common and readily available LLMs, Open AI's ChatGPT-4 and Google's Gemini Pro (1.0 Pro), in providing intraoperative decision support in plastic and reconstructive surgery procedures. Materials and Methods: We presented each LLM with 32 independent intraoperative scenarios spanning 5 procedures. We utilized a 5-point and a 3-point Likert scale for medical accuracy and relevance, respectively. We determined the readability of the responses using the Flesch-Kincaid Grade Level (FKGL) and Flesch Reading Ease (FRE) score. Additionally, we measured the models' response time. We compared the performance using the Mann-Whitney U test and Student's t-test. Results: ChatGPT-4 significantly outperformed Gemini in providing accurate (3.59 ± 0.84 vs. 3.13 ± 0.83, p-value = 0.022) and relevant (2.28 ± 0.77 vs. 1.88 ± 0.83, p-value = 0.032) responses. Alternatively, Gemini provided more concise and readable responses, with an average FKGL (12.80 ± 1.56) significantly lower than ChatGPT-4's (15.00 ± 1.89) (p < 0.0001). However, there was no difference in the FRE scores (p = 0.174). Moreover, Gemini's average response time was significantly faster (8.15 ± 1.42 s) than ChatGPT'-4's (13.70 ± 2.87 s) (p < 0.0001). Conclusions: Although ChatGPT-4 provided more accurate and relevant responses, both models demonstrated potential as intraoperative tools. Nevertheless, their performance inconsistency across the different procedures underscores the need for further training and optimization to ensure their reliability as intraoperative decision-support tools.

Clinical and Surgical Applications of Large Language Models: A Systematic Review.

Background: Large language models (LLMs) represent a recent advancement in artificial intelligence with medical applications across various healthcare domains. The objective of this review is to highlight how LLMs can be utilized by clinicians and surgeons in their everyday practice. Methods: A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Six databases were searched to identify relevant articles. Eligibility criteria emphasized articles focused primarily on clinical and surgical applications of LLMs. Results: The literature search yielded 333 results, with 34 meeting eligibility criteria. All articles were from 2023. There were 14 original research articles, four letters, one interview, and 15 review articles. These articles covered a wide variety of medical specialties, including various surgical subspecialties. Conclusions: LLMs have the potential to enhance healthcare delivery. In clinical settings, LLMs can assist in diagnosis, treatment guidance, patient triage, physician knowledge augmentation, and administrative tasks. In surgical settings, LLMs can assist surgeons with documentation, surgical planning, and intraoperative guidance. However, addressing their limitations and concerns, particularly those related to accuracy and biases, is crucial. LLMs should be viewed as tools to complement, not replace, the expertise of healthcare professionals.

Testicular Mixed Germ Cell Tumor (TMGCT) Management: Addressing Infection and Tumor Marker Dynamics.

We report the case of a 23-year-old male presenting with right testicular swelling, post-coital pain, and fever. Initial MRI and local examination suggested testicular carcinoma. Elevated serum alpha-fetoprotein (AFP) and lactate dehydrogenase (LDH) levels were observed. Biopsy confirmed a mixed germ cell tumor (MGCT). Concurrently, the patient was diagnosed with an infection and treated with antibiotics. Remarkably, following antibiotic therapy, fever resolved, and tumor marker levels significantly decreased. Subsequent orchidectomy confirmed the diagnosis of MGCT. This case underscores the importance of recognizing and treating concurrent infections, which may influence both clinical presentation and tumor marker levels in testicular germ cell tumors.

Impact of Methods of Preparation on Mechanical Properties, Dissolution Behavior, and Tableting Characteristics of Ibuprofen-Loaded Amorphous Solid Dispersions.

This study aims to improve the biopharmaceutical, mechanical, and tableting properties of a poorly soluble drug, ibuprofen (IBP), by preparing amorphous solid dispersion (ASD) followed by a sustained-release tablet formulation. A suitable polymer to develop an ASD system was chosen by utilizing the apparent solubility of IBP in various polymer solutions. ASDs containing various ratios of IBP and selected polymer were prepared by the melt fusion (MF) method. ASD containing optimized drug-polymer ratio prepared by freeze-drying (FD) method was characterized and compared physicochemically. The solubility of IBP in water increased 28-fold and 35-fold when formulated as ASD by MF and FD, respectively. Precise formulations showed amorphization of IBP and increased surface area, improving solubility. The dissolution pattern of optimized ASD-IBP in pH 6.8 phosphate buffer after 60 min in MF and FD was enhanced 3-fold. In addition, direct compression tablets comprising optimized ASD granules from MF and FD were made and assessed using compendial and noncompendial methods. ASD-IBP/MF and ASD-IBP/FD formulations showed a similar drug release profile. In addition, 12 h of sustained IBP release from the ASD-IBP-containing tablets was obtained in a phosphate buffer with a pH of 6.8. From the dissolution kinetics analysis, the Weibull model fitted well. The drug release pattern indicated minimal variations between tablets formed using ASD-IBP prepared by both procedures; however, pre- and postcompression assessment parameters differed. From these findings, the application of ASD and sustained-release polymers in matrix formation might be beneficial in improving the solubility and absorption of poorly soluble drugs such as IBP.

A RIPK1-specific PROTAC degrader achieves potent antitumor activity by enhancing immunogenic cell death.

Receptor-interacting serine/threonine-protein kinase 1 (RIPK1) functions as a critical stress sentinel that coordinates cell survival, inflammation, and immunogenic cell death (ICD). Although the catalytic function of RIPK1 is required to trigger cell death, its non-catalytic scaffold function mediates strong pro-survival signaling. Accordingly, cancer cells can hijack RIPK1 to block necroptosis and evade immune detection. We generated a small-molecule proteolysis-targeting chimera (PROTAC) that selectively degraded human and murine RIPK1. PROTAC-mediated depletion of RIPK1 deregulated TNFR1 and TLR3/4 signaling hubs, accentuating the output of NF-κB, MAPK, and IFN signaling. Additionally, RIPK1 degradation simultaneously promoted RIPK3 activation and necroptosis induction. We further demonstrated that RIPK1 degradation enhanced the immunostimulatory effects of radio- and immunotherapy by sensitizing cancer cells to treatment-induced TNF and interferons. This promoted ICD, antitumor immunity, and durable treatment responses. Consequently, targeting RIPK1 by PROTACs emerges as a promising approach to overcome radio- or immunotherapy resistance and enhance anticancer therapies.

Anti-EGFR Antibody-Drug Conjugate Carrying an Inhibitor Targeting CDK Restricts Triple-Negative Breast Cancer Growth.

PURPOSE: Anti-EGFR antibodies show limited response in breast cancer, partly due to activation of compensatory pathways. Furthermore, despite the clinical success of cyclin-dependent kinase (CDK) 4/6 inhibitors in hormone receptor-positive tumors, aggressive triple-negative breast cancers (TNBC) are largely resistant due to CDK2/cyclin E expression, whereas free CDK2 inhibitors display normal tissue toxicity, limiting their therapeutic application. A cetuximab-based antibody drug conjugate (ADC) carrying a CDK inhibitor selected based on oncogene dysregulation, alongside patient subgroup stratification, may provide EGFR-targeted delivery. EXPERIMENTAL DESIGN: Expressions of G1/S-phase cell cycle regulators were evaluated alongside EGFR in breast cancer. We conjugated cetuximab with CDK inhibitor SNS-032, for specific delivery to EGFR-expressing cells. We assessed ADC internalization and its antitumor functions in vitro and in orthotopically grown basal-like/TNBC xenografts. RESULTS: Transcriptomic (6,173 primary, 27 baseline, and matched post-chemotherapy residual tumors), single-cell RNA sequencing (150,290 cells, 27 treatment-naïve tumors), and spatial transcriptomic (43 tumor sections, 22 TNBCs) analyses confirmed expression of CDK2 and its cyclin partners in basal-like/TNBCs, associated with EGFR. Spatiotemporal live-cell imaging and super-resolution confocal microscopy demonstrated ADC colocalization with late lysosomal clusters. The ADC inhibited cell cycle progression, induced cytotoxicity against high EGFR-expressing tumor cells, and bystander killing of neighboring EGFR-low tumor cells, but minimal effects on immune cells. Despite carrying a small molar fraction (1.65%) of the SNS-032 inhibitor, the ADC restricted EGFR-expressing spheroid and cell line/patient-derived xenograft tumor growth. CONCLUSIONS: Exploiting EGFR overexpression, and dysregulated cell cycle in aggressive and treatment-refractory tumors, a cetuximab-CDK inhibitor ADC may provide selective and efficacious delivery of cell cycle-targeted agents to basal-like/TNBCs, including chemotherapy-resistant residual disease.

Impact of project managers emotional intelligence on megaprojects success through mediating role of human related agile challenges: Project management as a moderator.

Globalization and competition drive rapid adoption of new technologies, leading to a rise in complex projects. Project managers need to know how to lead teams through the planning and execution phases of a project while keeping in line with the organization's objectives. In order to successfully manage complex projects, emotional intelligence is an essential leadership quality. Therefore, the present research aimed to investigate the impact of project manager's emotional intelligence (PMEI) on megaprojects China-Pakistan Economic Corridor (CPEC) success through mediating roles of human-related agile challenges Inventory (HRACI) and project success factors (PSF), and project management as a moderator. The study employed convenience and purposive sampling methods to collect data from 533 project managers working on CPEC projects. The Smart PLS 4 software was utilized to evaluate the hypotheses. The results of this study indicated that the direct effect of a PMEI is not significant on CPEC. However, through mediating variables, HRACI exhibited a negative and significant association, while PSF positively and significantly mediate the relationship among PMEI and CPEC. Furthermore, project management as a moderator has a significant and positive effect on PMEI and PSF, however, insignificant between PMEI and CPEC, and negatively significant among PMEI and HRACI. The findings of this study are of great significance for project managers and project leaders. They will need to acquire the skills to prevent issues from arising, particularly when conflicts emerge, in order to ensure the success of megaproject. Therefore, current study recommend that PMEI appears to have a vital role in social interactions, promoting emotions of trust, efficient communication, and cooperation with other project teams in high-stress work environments like CPEC. Lastly, theoretical and practical contributions are discussed, as well as research constraints and future research directions.

AI in Hand Surgery: Assessing Large Language Models in the Classification and Management of Hand Injuries.

Background: OpenAI's ChatGPT (San Francisco, CA, USA) and Google's Gemini (Mountain View, CA, USA) are two large language models that show promise in improving and expediting medical decision making in hand surgery. Evaluating the applications of these models within the field of hand surgery is warranted. This study aims to evaluate ChatGPT-4 and Gemini in classifying hand injuries and recommending treatment. Methods: Gemini and ChatGPT were given 68 fictionalized clinical vignettes of hand injuries twice. The models were asked to use a specific classification system and recommend surgical or nonsurgical treatment. Classifications were scored based on correctness. Results were analyzed using descriptive statistics, a paired two-tailed t-test, and sensitivity testing. Results: Gemini, correctly classifying 70.6% hand injuries, demonstrated superior classification ability over ChatGPT (mean score 1.46 vs. 0.87, p-value < 0.001). For management, ChatGPT demonstrated higher sensitivity in recommending surgical intervention compared to Gemini (98.0% vs. 88.8%), but lower specificity (68.4% vs. 94.7%). When compared to ChatGPT, Gemini demonstrated greater response replicability. Conclusions: Large language models like ChatGPT and Gemini show promise in assisting medical decision making, particularly in hand surgery, with Gemini generally outperforming ChatGPT. These findings emphasize the importance of considering the strengths and limitations of different models when integrating them into clinical practice.

A comprehensive case study on successful multimodal therapy in philadelphia chromosome-positive acute myeloid leukemia with NPM1 and IDH2 mutations.

A 67-year-old female came to Tampa General Hospital with Philadelphia chromosome-positive (Ph+) acute myeloid leukemia (AML) featuring an intriguing combination of mutations, including NPM1 and IDH2 mutations. Novel combination therapy with azacitidine, venetoclax and ponatinib allowed her to successfully achieve a complete response (CR) and undergo an allogeneic hematopoietic stem cell transplant (HSCT). This case report provides an overview of her clinical course, emphasizing the significance of integrated therapy and the challenges associated with balancing treatment for AML. It also underscores the importance of a multidisciplinary approach and careful monitoring of patients with complex hematologic conditions.

Comparative Analysis of Artificial Intelligence Virtual Assistant and Large Language Models in Post-Operative Care.

In postoperative care, patient education and follow-up are pivotal for enhancing the quality of care and satisfaction. Artificial intelligence virtual assistants (AIVA) and large language models (LLMs) like Google BARD and ChatGPT-4 offer avenues for addressing patient queries using natural language processing (NLP) techniques. However, the accuracy and appropriateness of the information vary across these platforms, necessitating a comparative study to evaluate their efficacy in this domain. We conducted a study comparing AIVA (using Google Dialogflow) with ChatGPT-4 and Google BARD, assessing the accuracy, knowledge gap, and response appropriateness. AIVA demonstrated superior performance, with significantly higher accuracy (mean: 0.9) and lower knowledge gap (mean: 0.1) compared to BARD and ChatGPT-4. Additionally, AIVA's responses received higher Likert scores for appropriateness. Our findings suggest that specialized AI tools like AIVA are more effective in delivering precise and contextually relevant information for postoperative care compared to general-purpose LLMs. While ChatGPT-4 shows promise, its performance varies, particularly in verbal interactions. This underscores the importance of tailored AI solutions in healthcare, where accuracy and clarity are paramount. Our study highlights the necessity for further research and the development of customized AI solutions to address specific medical contexts and improve patient outcomes.

Artificial Intelligence Support for Informal Patient Caregivers: A Systematic Review.

This study aims to explore how artificial intelligence can help ease the burden on caregivers, filling a gap in current research and healthcare practices due to the growing challenge of an aging population and increased reliance on informal caregivers. We conducted a search with Google Scholar, PubMed, Scopus, IEEE Xplore, and Web of Science, focusing on AI and caregiving. Our inclusion criteria were studies where AI supports informal caregivers, excluding those solely for data collection. Adhering to PRISMA 2020 guidelines, we eliminated duplicates and screened for relevance. From 947 initially identified articles, 10 met our criteria, focusing on AI's role in aiding informal caregivers. These studies, conducted between 2012 and 2023, were globally distributed, with 80% employing machine learning. Validation methods varied, with Hold-Out being the most frequent. Metrics across studies revealed accuracies ranging from 71.60% to 99.33%. Specific methods, like SCUT in conjunction with NNs and LibSVM, showcased accuracy between 93.42% and 95.36% as well as F-measures spanning 93.30% to 95.41%. AUC values indicated model performance variability, ranging from 0.50 to 0.85 in select models. Our review highlights AI's role in aiding informal caregivers, showing promising results despite different approaches. AI tools provide smart, adaptive support, improving caregivers' effectiveness and well-being.

Assessment of textile effluent treatment by immobilized Trametes pubescens MB 89 for plant growth promotion.

Industrialization and the ever-increasing world population have diminished high-quality water resources for sustainable agriculture. It is imperative to effectively treat industrial effluent to render the treated water available for crop cultivation. This study aimed to assess the effectiveness of textile effluent treated with Trametes pubescens MB 89 in supporting maize cultivation. The fungal treatment reduced the amounts of Co, Pb and As in the textile effluent. The biological oxygen demand, total dissolved solids and total suspended solids were within the permissible limits in the treated effluent. The data indicated that the irrigation of maize with fungal-treated textile effluent improved the growth parameters of the plant including root, shoot length, leaf area and chlorophyll content. Moreover, better antioxidant activity, total phenol content and protein content in roots, stems and leaves of maize plants were obtained. Photosynthetic parameters (potential quantum yield, electron transport rate and fluorescence yield of non-photochemical losses other than heat) were also improved in the plants irrigated with treated effluent as compared to the control groups. In conclusion, the treatment of textile effluent with the immobilized T. pubescens presents a sustainable solution to minimize chemical pollution and effectively utilize water resources.

Evaluation of antihypertensives for post partum management of hypertensive disorders of pregnancy: A systematic review and meta-analysis.

BACKGROUND: There is a lack of evidence that directly shows the best antihypertensive treatment options for post partum management of the hypertensive disorders of pregnancy. Our objective was to analyze the safest and most effective antihypertensive drugs post partum for patients with hypertensive disorders of pregnancy. METHODS: PubMed, Cochrane, and MEDLINE were searched to find relevant articles published from inception to Feb 2024. We included randomized control trials, in English, featuring a population of postnatal women with hypertensive disorders of pregnancy or postpartum women with de novo hypertension with a follow-up of up to 6 months in which any antihypertensive medication was compared with Placebo or a comparison between different doses of antihypertensives was done. The statistical analyses were conducted using Review Manager with a random-effects model. RESULTS: Our analysis revealed that almost all antihypertensives are effective in treating postpartum hypertension. However, some medications had alternating roles in controlling specific outcomes. Using calcium channel blockers resulted in a faster time to sustain BP control than the control (SMD: -0.37; 95% CI: -0.73 to -0.01; P = 0.04). In contrast, using ACE inhibitors or ARBs demanded the use of other antihypertensives in contrast to all other drugs assessed (RR: 2.09; 95% CI: 1.07 to 4.07; P = 0.03). CONCLUSION: Timely management of the hypertensive disorders of pregnancy postpartum is life-saving. All the traditional antihypertensives we assessed effectively manage hypertension postpartum, thus allowing the physician to tailor the particular drug regimen according to the patient's needs and comorbidities without any hindrance.

Genomic characterization of human respiratory syncytial virus circulating in Islamabad, Pakistan, during an outbreak in 2022-2023.

In this study, conducted at the National Institute of Health, Islamabad, during an outbreak of human respiratory syncytial virus (hRSV) from December 2022 to January 2023, the first whole-genome sequences of hRSV isolates from Islamabad, Pakistan, were determined. Out of 10 positive samples, five were sequenced, revealing the presence of two genotypes: RSV-A (GA2.3.5, ON1 strain) and RSV-B (GB5.0.5.a, BA-10 strain). A rare non-synonymous substitution (E232G) in G the protein and N276S in the F protein were found in RSV-A. In RSV-B, the unique mutations K191R, Q209R, and I206M were found in the F protein. These mutations could potentially influence vaccine efficacy and viral pathogenicity. This research underscores the importance of genomic surveillance for understanding RSV diversity and guiding public health responses in Pakistan.