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Chloroquine-primaquine (CQ-PQ) continues to be the frontline therapy for radical cure of Plasmodium vivax malaria. Emergence of CQ-resistant (CQR) P. vivax parasites requires a shift to artemisinin combination therapies (ACTs), which imposes a significant financial, logistical, and safety burden. Monitoring the therapeutic efficacy of CQ is thus important. Here, we evaluated the therapeutic efficacy of CQ-PQ for P. vivax malaria in northeast Myanmar. We recruited 587 patients with P. vivax monoinfection attending local malaria clinics during 2012 to 2013. These patients received three daily doses of CQ at a total dose of 24 mg of base/kg of body weight and an 8-day PQ treatment (0.375 mg/kg/day) commencing at the same time as the first CQ dose. Of the 401 patients who finished the 28-day follow-up, the cumulative incidence of recurrent parasitemia was 5.20% (95% confidence interval [CI], 3.04% to 7.36%). Among 361 (61%) patients finishing a 42-day follow-up, the cumulative incidence of recurrent blood-stage infection reached 7.98% (95% CI, 5.20% to 10.76%). The cumulative risk of gametocyte carriage at days 28 and 42 was 2.21% (95% CI, 0.78% to 3.64%) and 3.93% (95% CI, 1.94% to 5.92%), respectively. Interestingly, for all 15 patients with recurrent gametocytemia, this was associated with concurrent asexual stages. Genotyping of recurrent parasites at the merozoite surface protein 3α gene locus from 12 patients with recurrent parasitemia within 28 days revealed that 10 of these were the same genotype as at day 0, suggesting recrudescence or relapse. Similar studies in 70 patients in the same area in 2007 showed no recurrent parasitemias within 28 days. The sensitivity to chloroquine of P. vivax in northeastern Myanmar may be deteriorating.

Heart rate variability (HRV) falls in humans with sepsis, but the mechanism is not well understood. We utilized a mouse model of endotoxemia to test the hypothesis that cytokines play a role in abnormal HRV during sepsis. Adult male C57BL/6 mice underwent surgical implantation of probes to continuously monitor electrocardiogram and temperature or blood pressure via radiotelemetry. Administration of high-dose LPS (Escherichia coli LPS, 10 mg/kg, n = 10) caused a biphasic response characterized by an early decrease in temperature and heart rate at 1 h in some mice, followed by a prolonged period of depressed HRV in all mice. Further studies showed that LPS doses as low as 0.01 mg/kg evoked a significant decrease in HRV. With high-dose LPS, the initial drops in temperature and HR were temporally correlated with peak expression of TNF[alpha] 1 h post-LPS, whereas maximal depression in HRV coincided with peak levels of multiple other cytokines 3-9 h post-LPS. Neither hypotension nor hypothermia explained the HRV response. Pretreatment with dexamethasone prior to LPS significantly blunted expression of 7 of the 10 cytokines studied and shortened the duration of depressed HRV by about half. Interestingly, dexamethasone treatment alone caused a dramatic increase in both low- and highfrequency HRV. Administration of recombinant TNF[alpha] caused a biphasic response in HR and HRV similar to that caused by LPS. Understanding the role of cytokines in abnormal HRV during sepsis could lead to improved strategies for detecting life-threatening nosocomial infections in intensive care unit patients. sepsis; lipopolysaccharide; dexamethasone; tumor necrosis factor alpha doi: 10.1152/ajpregu.00132.2009

Background: With the advent of artificial intelligence (AI) in healthcare, digital platforms like ChatGPT offer innovative alternatives to traditional medical consultations. This study seeks to understand the comparative outcomes of AI-assisted ChatGPT consultations and conventional face-to-face interactions among infertility patients., Methods: A cross-sectional study was conducted involving 120 infertility patients, split evenly between those consulting via ChatGPT and traditional face-to-face methods. The primary outcomes assessed were patient satisfaction, understanding, and consultation duration. Secondary outcomes included demographic information, clinical history, and subsequent actions post-consultation., Results: While both consultation methods had a median age of 34 years, patients using ChatGPT reported significantly higher satisfaction levels (median 4 out of 5) compared to face-to-face consultations (median 3 out of 5; p < 0.001). The ChatGPT group also experienced shorter consultation durations, with a median difference of 12.5 minutes (p < 0.001). However, understanding, demographic distributions, and subsequent actions post-consultation were comparable between the two groups., Conclusions: AI-assisted ChatGPT consultations offer a promising alternative to traditional face-to-face consultations in assisted reproductive medicine. While patient satisfaction was higher and consultation durations were shorter with ChatGPT, further studies are required to understand the long-term implications and clinical outcomes associated with AI-driven medical consultations. Key Messages What is already known on this topic:  Artificial intelligence (AI) applications, such as ChatGPT, have shown potential in various healthcare settings, including primary care and mental health support. Infertility is a significant global health issue that requires extensive consultations, often facing challenges such as long waiting times and varied patient satisfaction. Previous studies suggest that AI can offer personalized care and immediate feedback, but its efficacy compared with traditional consultations in reproductive medicine was not well-studied. What this study adds:  This study demonstrates that AI-assisted ChatGPT consultations result in significantly higher patient satisfaction and shorter consultation durations compared with traditional face-to-face consultations among infertility patients. Both consultation methods were comparable in terms of patient understanding, demographic distributions, and subsequent actions postconsultation. How this study might affect research, practice, or policy:  The findings suggest that AI-driven consultations could serve as an effective and efficient alternative to traditional methods, potentially reducing consultation times and improving patient satisfaction in reproductive medicine. Further research could explore the long-term impacts and broader applications of AI in clinical settings, influencing future healthcare practices and policies toward integrating AI technologies., (© The Author(s) 2024. Published by Oxford University Press on behalf of Fellowship of Postgraduate Medicine. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Low-dose metronomic (LDM) chemotherapy represents a new strategy to treat solid tumors by stronger antiangiogenic activity and less side-effects, especially in combination with other antiangiogenic agents. The aim of the study is to investigate the antiangiogenic effect of docetaxel alone and combined with (-)-epigallocatechin-3-gallate (EGCG) in preclinical settings of gastric cancer. BGC-823 human gastric cancer xenograft model was used, and tumor growth, side-effects of mice were closely monitored. Expression of vascular endothelial growth factor and CD31 were observed by immunohistochemistry, and microvessel density of the tumor tissues was assessed by CD31 immunohistochemical analysis. Our results indicated that LDM docetaxel inhibited angiogenesis and growth of gastric cancer with less toxicity, and the effects were further enhanced by the concurrent administration of EGCG. Our study, for the first time, rationally demonstrated that LDM docetaxel treatment used alone or combined with EGCG is effective and safe in preclinical settings of gastric cancer. Our data suggest that LDM docetaxel used alone or combined with EGCG may be an innovative and promising therapeutic strategy in the experimental treatment of human gastric cancer.

Objectives: To test the hypothesis that nearest-neighbor analysis adds to logistic regression in the early diagnosis of late-onset neonatal sepsis., Design: The authors tested methods to make the early diagnosis of neonatal sepsis using continuous physiological monitoring of heart rate characteristics and intermittent measurements of laboratory values. First, the hypothesis that nearest-neighbor analysis makes reasonable predictions about neonatal sepsis with performance comparable to an existing logistic regression model was tested. The most parsimonious model was systematically developed by excluding the least efficacious clinical data. Second, the authors tested the hypothesis that a combined nearest-neighbor and logistic regression model gives an outcome prediction that is more plausible than either model alone. Training and test data sets of heart rate characteristics and laboratory test results over a 4-y period were used to create and test predictive models., Measurements: Nearest-neighbor, regression, and combination models were evaluated for discrimination using receiver-operating characteristic areas and for fit using the Wald statistic., Results: Both nearest-neighbor and regression models using heart rate characteristics and available laboratory test results were significantly associated with imminent sepsis, and each kind of model added independent information to the other. The best predictive strategy employed both kinds of models., Conclusion: The authors propose nearest-neighbor analysis in addition to regression in the early diagnosis of subacute, potentially catastrophic illnesses such as neonatal sepsis, and they recommend it as an approach to the general problem of predicting a clinical event from a multivariable data set.