Category: Nevin Manimala

JMIR Form Res. 2025 May 22;9:e66954. doi: 10.2196/66954.

ABSTRACT

BACKGROUND: Dengue, a mosquito-borne disease, has been a health challenge in Singapore for decades. In 2020, during the COVID-19 pandemic, Singapore encountered a serious dengue outbreak and deployed various communication strategies to raise public awareness and mitigate dengue transmission.

OBJECTIVE: Drawing on the Crisis and Emergency Risk Communication (CERC) framework, this study examines how dengue-related messages communicated on Facebook (Meta) during the COVID-19 pandemic fall into the CERC themes. This study also seeks to understand how these themes differ between dengue outbreak (eg, 2020) and nonoutbreak years (eg, 2021). In addition, we explore how message themes on dengue changed across different CERC phases within the dengue outbreak year.

METHODS: We conducted a content analysis on 314 Facebook posts published by public health authorities in Singapore between January 1, 2020, and September 30, 2022. We conducted chi-square tests to examine the differences in message themes between the dengue outbreak and nonoutbreak years. We also conducted chi-square tests to examine how these message themes varied across 3 CERC phases during the dengue outbreak year.

RESULTS: Our findings suggest that during the dual epidemics of dengue and COVID-19, Singapore’s public health communication on dengue largely adhered to CERC principles. Dengue-related messaging, particularly regarding intelligence and requests for contributions, significantly varied between outbreak and nonoutbreak years. In addition, messages on general advisories and vigilance, as well as those on social and common responsibility, significantly differed across the CERC phases during the dengue outbreak year.

CONCLUSIONS: Singapore’s public health authorities flexibly adjusted their messaging strategies on social media platforms in response to the evolving dengue situation during the COVID-19 pandemic, demonstrating the high adaptability of the government’s health communication amid the dual epidemics. However, several areas for improvement should also be noted for future public health communication to mitigate dengue transmission.

PMID:40403298 | DOI:10.2196/66954

Oncology (Williston Park). 2025 May 12;39(4):141-147. doi: 10.46883/2025.25921040.

ABSTRACT

Leptomeningeal disease (LMD) is the spread of cancer cells to the arachnoid mater, pia mater, and cerebrospinal fluid. It occurs in 5% to 10% of solid organ cancers, with higher rates in breast, lung, and melanoma cancers. The prognosis for patients with LMD remains poor, with a median survival of 1.5 months without treatment and 2 to 3 months with treatment, despite advances in cancer treatment. This retrospective study included 64 patients with LMD with primary cancers represented in the diagnosis-specific Graded Prognostic Assessment (DS-GPA) at a single institution over 5 years. Patient characteristics, treatment, and overall survival (OS) data were collected. Statistical analyses included descriptive statistics, log-rank tests, and Cox proportional hazards regression models. The median OS for the 64 patients with LMD was 2.6 months, with no statistically significant differences among cancer types. Though not statistically significant, those with higher DS-GPA scores trended toward longer survival in breast and lung cancer cohorts. Patients with LMD on imaging confined to 1 location (cerebrum, cerebellum, spine, or cranial nerves) and receiving systemic chemotherapy alone also had longer survival. The DS-GPA tool is promising for LMD prognostication and may be strengthened by incorporating imaging and chemotherapy characteristics. Larger, multicenter studies are needed to validate its prognostic utility. Keywords: Leptomeningeal disease, diagnosis-specific graded prognostic assessment, prognosis, overall survival, breast cancer, lung cancer.

PMID:40403292 | DOI:10.46883/2025.25921040

Plant Dis. 2025 May 22. doi: 10.1094/PDIS-11-24-2285-PDN. Online ahead of print.

ABSTRACT

Soybean (Glycine max L. Merr.) is a highly economically valuable crop grown extensively worldwide. However, it is prone to reduced yield and quality due to pests and diseases. A novel leaf spot disease was discovered during a soybean disease survey conducted in 2022 within a field situated at N 46°50’5.05″ E 126°30’9.77″ (Liu et al., 2023). The disease has a high incidence in the field, with an incidence rate of 23%. Affected areas turn yellow and are distributed in spots on the leaves. Severe cases develop into patches of dark green tissue lesions, and the leaves may become yellow, wilt, or even fall off. It was observed that the incidence rate in the shade under the trees was higher than that in direct sunlight, which affects soybean leaves and severely impacts their photosynthetic ability. To investigate this disease, a total of 30 diseased soybean plants were sampled from a continuous field. The infected leaf tissues were disinfected and rinsed, after which the pathogen was isolated using the single spore isolation method. After obtaining the purified pathogen, it was cultured on a PDA plate at 28°C for 7 days for future use. Molecular identification was conducted using complete rDNA-ITS sequences with primer pairs ITS1/ITS4, ACT1/ACT2, and MS1/MS2 (Raja et al., 2017). The amplification system comprised 25 µL: 12.5 µL PCR Mix, 1.5 µL DNA template, 1 µL of each upstream and downstream primer, and 9 µL ddH2O. The PCR amplification reaction conditions were as follows: pre-denaturation at 95°C for 2 minutes, denaturation at 95°C for 30 seconds, annealing at 60°C for 30 seconds, extension at 72°C for 45 seconds, final extension at 72°C for 6 minutes, and storage at 4°C, with a total of 30 cycles. The PCR products were sent to Shenggong Bioengineering (Shanghai) Co., Ltd. for sequencing. The sequences were deposited in GenBank with accession numbers OR237554 (ITS), PQ336777 (ACT), and OR137984 (MS), respectively. In this study, three diffeent genes were used to identify the species of this fungus, confirming the reliability of the strain identification results. The maximum likelihood tree revealed that the isolate clustered with representative isolates of Cladosporium cladosporioides with 96% bootstrap support (Supplement Figure 1). Thus, the isolate was identified as Cladosporium cladosporioides. based on these results. This fungus grows relatively slowly and changes from light green to dark green (Figure 1). The fungus was cultured on PDA medium for microscopic observation. Conidia were abundant, transparent, light green, oval or fusiform, measuring 2 to 3 µm × 6 to 7 µm (n=50); macroconidia were less numerous, mostly two-septate, cylindrical, measuring 2 to 3 µm × 48 µm to 50 µm (n=50). The results showed various spore types and mycelial conditions. Pathogenicity experiments were first conducted using leaf. Leaves were disinfected, inoculated with fungal cakes, and incubated at 28°C for five days. The results showed a 100% leaf incidence, with symptoms consistent with leaf spot disease observed in the field. Another experiment was conducted using potted plants with three different inoculation concentrations. After the potted plants grew for two weeks, fungal spore suspensions with concentrations of 1×106, 1×107, and 1×108 were sprayed on the leaves of soybean seedlings every two days, at a volume of 1 mL per pot each time. Symptoms began to appear after three sprays. There were no symptoms observed in the leaves of the blank control group. Six pots of potted plants were planted at each concentration, with six soybean plants per pot, resulting in a total of 36 soybean plants sprayed seven times. The three concentrations of soybean potted plants began to develop diseases, which were similar to those observed in the field. According to statistics, the incidence rate was 60%. Therefore, the pathogenicity of this pathogen is high and seriously affects the growth and yield of soybean plants, necessitating further research on this pathogen. The experimental results were consistent with Koch’s postulates. This study is the first to report Cladosporium cladosporioides causing leaf spot disease in soybean in China.

PMID:40403275 | DOI:10.1094/PDIS-11-24-2285-PDN

Am J Nurs. 2025 Jun 1;125(6):52-58. doi: 10.1097/AJN.0000000000000092. Epub 2025 May 22.

ABSTRACT

BACKGROUND: Hospital-at-home (HaH) programs provide hospital-level care in the home as an alternative to inpatient hospital stays. Because no standard system exists for storing and organizing medications during home hospitalization, the risk of medication errors and potential harm may be increased. The medication errors workgroup of an HaH program at a quaternary care academic medical center identified the need for a medication storage system to mitigate errors resulting from misplaced or misused medications.

PURPOSE: An interdisciplinary quality improvement (QI) project initiated by the medication errors workgroup aimed to support safe, patient-friendly medication management and reduce medication errors.

METHODS: A low-technology medication storage system for HaH patients was designed and implemented in August 2022. Medication errors were compared before and after the intervention. A survey assessed patient and staff satisfaction with the storage system in the postintervention period.

RESULTS: Unadjusted analysis showed that, among the 552 patients admitted to our HaH program during the study period (January through December 2022), the risk of medication error was significantly lower (odds ratio [OR], 0.55; P = 0.046) in the postintervention group (n = 260) than in the preintervention group (n = 292). After adjustment for age and HaH duration, the risk of medication error remained lower with use of the intervention (OR, 0.52; P = 0.03). Most patients and health care workers who participated in the satisfaction survey responded positively to the project.

CONCLUSION: HaH programs have unique risks for medication errors that require program-specific solutions. This QI project developed a medication storage system that improved HaH medication administration. These results are promising and may further improve medication management in the home.

PMID:40403272 | DOI:10.1097/AJN.0000000000000092

Am J Nurs. 2025 Jun 1;125(6):22-27. doi: 10.1097/AJN.0000000000000082. Epub 2025 May 22.

ABSTRACT

BACKGROUND: Hypoglycemic episodes are among the most common adverse events experienced by hospitalized patients, occurring in up to half of all inpatients, with or without diabetes. Studies have shown that patients who have episodes of severe hypoglycemia while hospitalized have higher rates of 30-day readmission and postdischarge mortality. The Centers for Medicare and Medicaid Services considers hypoglycemic episodes to be a hospital-acquired condition. The cost burden of poor glycemic control in acute care settings is undeniably substantial.

PURPOSE: The aim of this study was to identify predictors of hypoglycemia among inpatients with type 2 diabetes.

METHODS: This retrospective descriptive correlational study involved abstracting data from 2019 to 2021 electronic health records for 600 hospitalized patients. Data were analyzed using descriptive and associative statistics and multivariate logistic regression.

RESULTS: Data analysis revealed that patients who were Asian or Hispanic, had a history of hypoglycemia, were taking both insulin and a sulfonylurea, or had a podiatric or renal admitting diagnosis had significantly higher odds of experiencing hypoglycemic episodes while hospitalized. The odds of 30-day readmission were significantly lower for patients who received diabetes self-management education, had higher glomerular filtration rates, or were admitted with a diagnosis of bone fracture.

CONCLUSION: The study findings can be used to help health care institutions design and implement more effective policies and procedures to prevent or mitigate hypoglycemic episodes.

PMID:40403266 | DOI:10.1097/AJN.0000000000000082

Am J Nurs. 2025 Jun 1;125(6):13-14. doi: 10.1097/AJN.0000000000000094a. Epub 2025 May 22.

ABSTRACT

Researchers say the cause is fewer staffed beds, not more patients.

PMID:40403253 | DOI:10.1097/AJN.0000000000000094a

PLoS One. 2025 May 22;20(5):e0323619. doi: 10.1371/journal.pone.0323619. eCollection 2025.

ABSTRACT

This study investigates the determination of stratification points for two study variables within the framework of simple random sampling, with a focus on estimating the population mean using a closely related auxiliary variable. Employing a superpopulation model, the research aims to minimize overall variance by deriving simplified equations that enhance the precision of parameter estimates. Instead of categorizing variables, the study emphasizes continuous variables to establish optimal strata boundaries (OSB), which are essential for creating homogeneous groups within each stratum. This stratification leads to more efficient sample sizes (SS) and improved accuracy in parameter estimation. However, achieving optimal OSB and SS poses challenges in scenarios with a fixed total sample size, such as survey designs constrained by limited budgets. To address this, the study proposes a robust methodology for calculating OSB and SS, leveraging knowledge of the survey’s per-unit stratum measurement costs or its probability density function. An empirical application of the method is demonstrated using breast cancer data, where the mean perimeter is estimated based on mean radius and mean texture. Additionally, hypothetical examples using Cauchy and standard power distributions are provided to illustrate the versatility of the proposed approach. The newly developed method has been integrated into the updated stratifyR package and implemented in LINGO software, facilitating its practical application. Comparative analysis reveals that this approach consistently outperforms or matches existing methods in enhancing the precision of population parameter estimation. Furthermore, simulation studies confirm its higher relative efficiency, making it a valuable contribution to the field of stratified sampling.

PMID:40403232 | DOI:10.1371/journal.pone.0323619

ACS Synth Biol. 2025 May 22. doi: 10.1021/acssynbio.5c00129. Online ahead of print.

ABSTRACT

DNA logical circuits can be applied to accurate classification of cancer status, benefiting from their excellent biocompatibility and parallelism. However, the existing cancer diagnosis models based on DNA logic circuits mainly adopt a linear structure, which makes it difficult to fully capture the complex nonlinear distribution characteristics in the disease data. In addition, DNA logic circuits cannot directly sense the expression levels of microRNAs (miRNAs). Here, we constructed a nonlinear classifier based on DNA logic circuits with the random forest algorithm. The classifier can directly sense the expression level of miRNAs in serum samples without isolating specific miRNAs and transmit the signals to the logic classification module and complete the nonlinear classification of cancer status. We validated the classification performance of the constructed nonlinear classifiers by using miRNA expression level samples to diagnose adenocarcinoma, ductal and lobular neoplasms, and squamous cell carcinoma with accuracies of 95.4%, 96.6%, and 97.2%, respectively. The classification results generated using the nonlinear classifiers based on DNA logic circuits showed a strong agreement with the actual disease states labeled in TCGA, as well as with the random forest algorithm, and had high parallelism and stability in the multiclassification of three different cancers. This work shows the great potential of DNA logic circuit-based nonlinear classifiers in cancer diagnosis, which provides a new approach to design efficient, accurate, and intelligent integrated disease diagnosis schemes.

PMID:40403203 | DOI:10.1021/acssynbio.5c00129

Am Soc Clin Oncol Educ Book. 2025 Jun;45(3):e473590. doi: 10.1200/EDBK-25-473590. Epub 2025 May 22.

ABSTRACT

The integration of artificial intelligence (AI) and machine learning (ML) in oncology clinical trials is rapidly evolving alongside the broader field. For example, AI-driven adaptive trial designs may allow for real-time modifications based on emerging safety and efficacy signals, enabling more responsive and efficient trials. AI-powered diagnostic tools, including radiomics, computational pathology, and spatial omics, can improve trial patient selection and response assessments. ML-based patient outcome simulations can similarly enhance patient stratification strategies and statistical power. Application of AI can also improve the accessibility of real-world data, including opportunities to enhance data extraction, standardization, and harmonization of data from routine clinical practice. Data generated from digital health technologies (eg, wearable devices, electronic sensors, computing platforms, software applications) may enable a more comprehensive understanding of patient populations to support clinical trials from enrollment to assessment. Automation of trial operations and data management can also improve data fidelity and decrease investigator burden, which has the potential to streamline trial execution and increase potential use of decentralization. There are ongoing efforts to enhance regulatory clarity, mitigate bias, and uphold ethical use of these novel technologies. In this article, we review use cases of AI and ML in oncology clinical trials, including their role in patient recruitment, trial design and operations, data management, and diagnostics. Although these technologies can have applications across all phases of drug development including early discovery, we focus on phase II and III trials, where AI and ML may have a pronounced ability to enhance trial efficiency, patient stratification, and regulatory decision making. By integrating AI and ML, clinical trials can become more adaptive, data-driven, and inclusive in the pursuit of improving patient outcomes.

PMID:40403202 | DOI:10.1200/EDBK-25-473590

JCO Glob Oncol. 2025 May;11:e2400625. doi: 10.1200/GO-24-00625. Epub 2025 May 22.

ABSTRACT

PURPOSE: Integrating palliative care into oncology is essential, yet disparities in access and quality persist, particularly in low- and middle-income countries (LMICs). The ASCO guidelines advocate for early, routine, interdisciplinary palliative care for patients with advanced cancer. Barriers to implementing these recommendations include resource limitations, inadequate training, and cultural perceptions. Recognizing these challenges is essential for improving equitable access to palliative care worldwide.

METHODS: This prospective survey assessed adherence to ASCO recommendations for palliative care integration among LMIC health care providers (HCPs). Participants were recruited via e-mail, social media, and a list of members involved in the ASCO Palliative Care Communities of Practice from February to May 2024. The survey included sections on sociodemographic information, self-perceived adherence to ASCO guidelines on a 5-point Likert scale, and open-ended questions on implementation barriers. Data were collected using Research Electronic Data Capture system. Participants were grouped by WHO regions. Descriptive statistics were used to summarize characteristics and adherence scores, and chi-square tests were used to evaluate regional differences. Thematic analysis identified key themes from open-ended responses.

RESULTS: One hundred eighty HCPs participated; 62% was female, and 51.1% was age 35-44 years. Most were physicians (66%), and 50% lacked palliative care specialization. Adherence to ASCO guidelines varied, with early palliative care referrals ranging from 50% in the Americas region to 0% in the Western Pacific region. Key barriers included lack of policy support (25%), unmet educational needs (22%), and accessibility constraints (19%).

CONCLUSION: Addressing identified barriers through evidence-based advocacy, comprehensive policy changes, training, and continuing education programs is essential for integrating palliative care into oncology services across LMICs, promoting health equity for patients with cancer.

PMID:40403199 | DOI:10.1200/GO-24-00625