Rapid Health Assessment Research Articles

Artificial Intelligence in Point-of-Care Biosensing: Challenges and Opportunities.

The integration of artificial intelligence (AI) into point-of-care (POC) biosensing has the potential to revolutionize diagnostic methodologies by offering rapid, accurate, and accessible health assessment directly at the patient level. This review paper explores the transformative impact of AI technologies on POC biosensing, emphasizing recent computational advancements, ongoing challenges, and future prospects in the field. We provide an overview of core biosensing technologies and their use at the POC, highlighting ongoing issues and challenges that may be solved with AI. We follow with an overview of AI methodologies that can be applied to biosensing, including machine learning algorithms, neural networks, and data processing frameworks that facilitate real-time analytical decision-making. We explore the applications of AI at each stage of the biosensor development process, highlighting the diverse opportunities beyond simple data analysis procedures. We include a thorough analysis of outstanding challenges in the field of AI-assisted biosensing, focusing on the technical and ethical challenges regarding the widespread adoption of these technologies, such as data security, algorithmic bias, and regulatory compliance. Through this review, we aim to emphasize the role of AI in advancing POC biosensing and inform researchers, clinicians, and policymakers about the potential of these technologies in reshaping global healthcare landscapes.

Sum or mean in calculation of qualitative scoring methods using the Dragonfly Biotic Index, and an alternative approach facilitating conservation prioritization

Qualitative scoring methods are tools for rapid freshwater health assessments. Total score is often calculated as the sum or mean of the values of the species involved, with minor nuances in interpretation, but with significant implications. We used the Dragonfly Biotic Index (DBI) calculated on Central European odonate species to demonstrate these implications. Each species within a community has a score ranging from 0 (widespread generalists) to 9 (sensitive specialists). A total score is calculated as the sum of the scores of all species (DBIsum) or is calculated by dividing by species richness (DBImean). Despite this duality, there has been little debate on either approach. Using simulated scenarios (high vs low richness, presence or absence of high- or low-scoring species), we tested the implications of DBIsum and DBImean and suggested a total score calculation for conservation prioritization based on permutation. This algorithm shows the percentile of a community compared to a set of randomly assembled communities of the same species richness. We also present the ‘dragDBI’ package for the statistical software R, a tool for more automated DBI-based environmental health assessments. Our permutational calculation is applicable to other macroinvertebrate-based scoring methods, such as the Biological Monitoring Working Party and the Average Score Per Taxon.

Automated body length and body condition measurements of whales from drone videos for rapid assessment of population health

AbstractMonitoring body length and body condition of individuals helps determine overall population health and assess adaptation to environmental changes. Aerial photogrammetry from drone‐based videos is a valuable method for obtaining body length and body condition measurements of cetaceans. However, the laborious manual processing of drone‐based videos to select frames to measure animals ultimately delays assessment of population health and hinders conservation actions. Here, we apply deep learning methods to expedite the processing of drone‐based videos to improve efficiency of obtaining important morphological measurements of whales. We develop two user‐friendly models to automatically (1) detect and output frames containing whales from drone‐based videos (“DeteX”) and (2) extract body length and body condition measurements from input frames (“XtraX”). We use drone‐based videos of gray whales to compare manual versus automated measurements (n = 86). Our results show automated methods reduced processing times by one‐ninth, while achieving similar accuracy as manual measurements (mean coefficient of variation <5%). We also demonstrate how these methods are adaptable to other species and identify remaining challenges to help further improve automated measurements in the future. Importantly, these tools greatly speed up obtaining key morphological data while maintaining accuracy, which is critical for effectively monitoring population health.

Stretchable Photonic Crystal-Assisted Glycoprotein Identification for Ovarian Cancer Diagnosis.

Photonic crystals with specific wavelengths can realize surface-enhanced excitation and emission intensities of fluorophores and enhance the fluorescence signals of fluorescent molecules. Herein, stretchable photonic crystals with good mechanochromic properties provide continuously adjustable forbidden wavelengths by stretching to change the lattice spacing, with reflectance peaks blue-shifted up to 110 nm to match indicators of different wavelengths and produce differentiated optical enhancement effects. Glycoproteins are significantly identified as clinical markers. However, the wide participation of glycoproteins in various life processes poses enormous complexity and critical challenges for rapid, facile, high-throughput, and accurate clinical analysis or health assessment. In this work, we proposed a stretchable photonic crystal-assisted glycoprotein identification approach for early ovarian cancer diagnosis. Stretchable photonic crystals can provide rich optical information to efficiently identify glycoproteins in complex matrices. A double-indicator fluorescence sensor was designed to respond to the protein trunk and oligosaccharide segment of glycoproteins separately for improved recognition accuracy. Seven typical glycoproteins could be discriminated from proteins, saccharides, or mixture interferents. Clinical ovarian cancer samples for early, intermediate, and advanced ovarian cancer and healthy subjects were verified with 100% accuracy. This strategy of stretchable photonic crystal-assisted glycoprotein identification provides an effective method for accurate, rapid ovarian cancer diagnosis and timely clinical treatment.

System Design and Implementation for Machine Learning and Internet of Things Based Anomaly Detection in Patient Movement

Emerging trends in healthcare have seen an increased need for improved monitoring, and as a result, machine learning (ML) and the Internet of Things have been integrated to develop smart systems that can provide rapid and accurate health assessments. With an inclusive view on mobility anomaly detection in patients, this paper aims to improve hospital patient safety. Patients wear or place these things, and the IoT sensor network then gathers real-time motion data. The information is then moved to the central server. The system was taught the machine learning part using a large number of normal and abnormal patient movement data. It also shows a way to differentiate between common activities and those that might indicate ill health. It comprises an IoT sensor network, a data processing unit, and an ML anomaly discovery module. There are sensors on the Internet of Things that have a wide range of movement data. Then, these are run through an ML model that captures the spatial-temporal dependencies between different sets based upon more sophisticated techniques like the Naive Bayes Classifier. Where there are abnormalities discovered after analyzing them, an alert is sent out; this will help in quick emergency response by saving lives even before diseases become last-stage acute conditions

Android-based Mobile Rapid Health Assessment in the Disaster Emergency Response Phase

Introduction : Rapid health assessment (RHA) is an assessment during the disaster emergency response phase that requires a fast, precise, and accurate time so that the damage caused by the disaster and immediate needs of disaster victims can be recognized. Rapid assessment with manual forms will prolong the process of needs assessment and analysis. Android-based RHA applications are made to facilitate rapid assessment so that the type of assistance needed can be determined as soon as possible. The purpose of the study was to create an android-based RHA application, test the validity and usability of the application, and test it on application users.
 Methods: This research was conducted using a Research and Development design divided into two stages. In stage 1, the application was created, and five experts tested the validity and reliability. Stage 2 was socialization and application trials on 30 disaster volunteers in Lamongan. In this stage, the usability of application was measured by the System Usability Scale.
 Results: The application components had good validity (I-CVI=1) and reliability (Cronbach’s alpha=0.768). The usability score showed that 84.3% of the users considered the application easy to operate, 83.67% stated that the menu display and features were easy to use, 80% stated that it was comfortable, 81.3% were satisfied with the application features, and 85.7% stated that the application was helpful in disaster.
 Conclusions: The RHA application is valid, reliable, and usable for general use for rapid health assessment in the disaster emergency response phase.

Prevalence and risk factors of post-traumatic stress disorder symptoms among Chinese health care workers following the COVID-19 pandemic

In December 2019, coronavirus disease 2019 (COVID-19) appeared in Wuhan (Hubei, China) and subsequently swept the globe. In addition to the risk of infection, there is a strong possibility that post-traumatic stress disorder (PTSD) may be a secondary effect of the pandemic. Health care workers (HCWs) participating in the pandemic are highly exposed to and may bear the brunt out of stressful or traumatic events. In this cross-sectional study, we assessed the morbidity and risk factors of PTSD symptoms among Chinese HCWs. A total of 457 HCWs were recruited from March 15, 2020, to Mach 22, 2020, including HCWs in Wuhan and Hubei Province (excluding Wuhan), the areas first and most seriously impacted by COVID-19. The morbidity of PTSD symptoms was assessed by the Event Scale–Revised (IES-R). The risk factors for PTSD symptoms were explored by means of logistic regression analysis. Over 40% of the respondents experienced PTSD symptoms more than one month after the COVID-19 outbreak, and this proportion increased to 57.7% in Wuhan HCWs, especially females and HCWs on the frontline. Thus, rapid mental health assessment and effective psychological interventions need to be developed for frontline HCWs to prevent long-term PTSD-related disabilities. Moreover, Negative coping style and neuroticism personality may be regarded as high risk factors for PTSD symptoms. Improving individual coping strategies to enhance resilience should be the focus of further preventive intervention strategies.

Biomonitoring by using Rapid-Read Pathogenic Bacteria Indicator in Sediments and Bivalve Mollusks: Southern Gulf of Thailand, a Mangrove Area Case Study

Pathogenic bacteria groups can be applied to be rapid bioindicator for environmental assessment. The objective of study was to monitor 5 groups of indicator and pathogenic bacteria (Escherichia coli/ total coliform, Salmonella spp., Vibrio spp., Bacillus spp. and Clostridium spp.) in surface water sediments collected before monsoon and after monsoon season from 6 areas located in a Thepha District, Songkhla province mangrove closed to Southern gulf of Thailand. Each collected area were selected in criteria of totally different in environment parameter and tide. The relative levels of total coliforms, and E. coli, were typically increased in rainy season (July) compared with winter and summer at most sites. In parallel, the highest number of Vibrio spp. was found in summer (April). The highest number of E. coli and Coliform were detected during rainy season at the estuary area with low oxygen dissolved, the average numbers were 6.6×103 and 3.8×105 CFU/g, respectively. Coliform number shows negative correlate with dissolved oxygen (r2 = 0.756, p < 0.05). In addition, total coliform, Bacillus spp. and Clostridium spp. were found in bivalve mollusks during rainy season but the total was not over standard. A very strong positive correlation was found between the temperature and the number of Vibrio spp. and Bacillus spp. The number presence of Vibrio spp. has a strong positive correlation with pH, the number of Bacillus spp. and number of Clostridium spp. In contrast, Vibrio spp. number was strongly negative correlated with water salinity (r2 = 0.632, p < 0.05). Salmonella species were detected only 2 area around shrimp farm in between December of a year-round. In this study area, there are no evidence that the number of Salmonella and Clostridium species founded related to changing of season. This is the first baseline data as an alternative guide concept for the determination of bacterial indicators. Pathogenic bacteria number can be applied to be a key factors for a rapid health assessment for villager around study area that related to EIA in the near future. HIGHLIGHTS The common problem in biomonitoring process in environment are limit of biological indicator and less data collected to establish relative between bioindicator and environmental parameters Key pathogenic bacteria groups were selected and analyzed conducted in order to find the rapid-read method for biomonitoring and assessment This is the first baseline data as an alternative guide concept for the determination of bacterial indicators. Pathogenic bacteria number can be applied to be a key factors for a rapid health assessment for villager around study area that related to EIA in the near future GRAPHICAL ABSTRACT

Rapid and Collaborative Population Health Assessment for People Experiencing Homelessness in Toronto: The CARE Program.

Rapid and Collaborative Population Health Assessment for People Experiencing Homelessness in Toronto: The CARE Program.

Epidemiology of TB in prisoners: a metanalysis of the prevalence of active and latent TB

BackgroundTuberculosis (TB) in prisons usually occurs at higher rates than in the general population, especially in developing countries. TB has been reported as the most common cause of death among prisoners. Studies have shown limitations for early detection of TB in prisons that seem to result from mistaken concepts about TB, delayed diagnosis mainly due to the naturalization of lack of healthcare for this populationMethodsA scoping review was performed using the methodology of the Joanna Briggs Institute to assess “What are the scientific evidences on the epidemiology of TB in the prison system?”. Then, a meta-analysis was performed to assess the prevalence of TB (active and latent) TB in prisoners. The results are presented as prevalence, in percentage, through random effects models, with a confidence interval of 95%.ResultsRegarding active TB, the results of the metanalysis showed that countries with a high burden of TB had a prevalence of 3.54% [2.71; 4.63], countries not considered to be high burden TB countries had a prevalence of 1.43% [0.86; 2.37]. Latent TB had a prevalence of 51.61% [39.46; 63.58] in high TB burden countries and a prevalence of 40.24% [23.51; 59.61] in countries with low TB burden. In terms of development, in low- and lower-middle-income countries, the prevalence of active TB was 3.13% [1.84; 5.29] and in high- and upper-middle income countries the prevalence was 2.25% [1.70; 2.99]. The prevalence of latent TB in high- and middle-income countries was 43.77% [28.61; 60.18] and of 49.42% [45.91; 52.94] in low and lower middle-income countries.ConclusionOur analysis suggests that TB, and probably other infectious diseases, find fertile ground in prisons where previous acquire social disadvantages seem to thrive—therefore, TB in prisons is a global public health problem and effective strategies are needed to control the disease are needed targeting the prison environment, including rapid health assessments to understand each context and to implement tailored and precision interventions.

Explainable Deep-Learning-Assisted Sweat Assessment via a Programmable Colorimetric Chip.

Multianalytes and individual differences of biofluids (such as blood, urine, or sweat) pose enormous complexity and challenges to rapid, facile, high-throughput, and accurate clinical analysis or health assessment. Deep-learning (DL)-assisted image analysis has been demonstrated to be an efficient big data process which shows accurate individual identification. However, the data-driven "black boxes" of current DL algorithms are suffering from the nontransparent inner working mechanism. In this work, we designed a programmable colorimetric chip with explainable DL to approach accurate classification and quantification analysis of sweat samples. Gel (sodium alginate) capsules with different indicators were adopted to combinate as designed programmable colorimetric chips. We collected 4600 colorimetric response images as the data set and assessed two DL algorithms and seven machine learning (ML) algorithms. Glucose, pH, and lactate in human sweat could be facilely and 100% accurately classified and quantified by the convolutional neural network (CNN) DL algorithm, and the testing results of actual sweat via the DL-assisted colorimetric approach match 91.0-99.7% with the laboratory measurements. Class activation mapping (CAM) was processed to visualize the inner working mechanism of CNN operation, which could help to verify and explicate the design rationality of colorimetric chips. The explainable DL-assisted programmable colorimetric chip provided an "end-to-end" strategy to ascertain the black box of the DL algorithm, promoted software design or principium optimization, and contributed facile indicators for clinical monitoring, disease prevention, and even new scientific discoveries.

First description of the Burmeister porpoise (Phocoena spinipinnis) electrocardiogram.

The aim of this study was to make the first characterization of the electrocardiogram (ECG) of the Burmeister porpoise (Phocoena sipinipinnis). A total of three ECGs were made in one, wild juvenile female (body weight = 32.5 kg, Lt = 1.45 m), temporarily held in captivity for rehabilitation at Mar del Plata Aquarium (Argentina). ForECG recordings, the animal was removed from its pool on a stretcher under constant veterinary supervision. A traditional six-lead system in the frontal plane was used, with the typical four "extremities" electrodes positioned bilaterally. ECGs were readable and all waves were detectable. The QRS complexes were characterized by Q wave absence in all leads. The ECG characteristically displayed distinctly defined STs, with a T wave clearly differentiated from the QRS complex. An STs elevation was detected in leads II and III. The mean electrical axis was -79°, meaning a cranial left axis orientation. The animal showed normal sinus rhythm and heart rate (HR) showed a continuous oscillation between tachycardia and bradycardia. Mean HR from all recordings was 147.02 ± 24.95 bpm (range = 71-176, n = 178). Evidence of the occurrence of respiratory sinus arrhythmia was detected through HR variations and RR interval differences, which suggested an adequate cardiovascular system compensation to handling conditions. This study has reported ECG amplitude and duration measurements of a potentially healthy specimen, which represents the starting point for the determination of normal ECG values for this species. Also, it has confirmed that ECG is a valuable noninvasive tool for rapid cardiac health assessment in small cetaceans.

Occupational Health and Safety of Immigrant Hotel Housekeepers.

The accommodations sector is one of the largest employers of immigrant and minority workers in the United States. Hotel housekeepers represent the industry's largest workforce while facing difficult work conditions, health hazards, and psychological stress. This is one of the few empirical studies that address the working conditions of housekeepers in the United States and their perspective of health challenges they face. A cross-sectional survey study was conducted with hotel housekeepers (N = 140) in Florida and in collaboration with the local labor union as a rapid assessment of occupational health and safety risk exposures, work-related injuries, coping mechanisms, and perceived management responses. Experienced union workers recruited study participants and administered interviewer-administered surveys. The analysis included descriptive statistics and multivariate regression. Sampled hotel housekeepers were found to work under significant time pressures to complete excessive workloads and to experience chemical and biological exposures and physical and psychosocial strains. Poor work equipment/insufficient supplies had a negative impact on hotel housekeepers' health, these include heavy wet towels vacuum cleaners having a significant effect on (p < .001) sprains and strains. Poor cleaning supplies had a significant effect (p < .001) on chemical burns. Other significant findings are provided in the article. While housekeeping work conditions include many challenges, relatively simple changes by hotels' management can provide potential relief and improve workers' health and safety outcomes, such as functional equipment, sufficient inventory, management support, and proper rest breaks-subsequently increasing workers' health and reducing accidents, and thus potentially improving productivity at a relatively low cost.

High‐resolution water‐quality and ecosystem‐metabolism modeling in lowland rivers

AbstractHigh‐resolution monitoring of water quality and ecosystem functioning over large spatial scales in expansive lowland river catchments is challenging. Therefore, we need modeling tools to predict these processes at locations where observations are absent. Here, we present a new approach to estimate ecosystem metabolism underpinned by a high‐resolution, process‐based model of in‐stream flows and water quality. The model overcomes the current challenges in metabolism modeling by accounting for oxygen transport under varying flows and oxygen transformations due to biogeochemical processes. We implement the model in a 62‐km‐long stretch of the River Thames, England, using observations spanning 2 yr. Model outputs suggest that the river is primarily autotrophic from mid‐spring to mid‐summer due to high biomass during low‐flow periods, and is heterotrophic during the rest of the year. Ecosystem respiration in upstream reaches is driven mainly by biochemical oxygen demand, autotrophic respiration, and nitrification processes, whereas downstream sites also show a control of benthic oxygen demand in addition to the aforementioned processes. Using empirical modeling, we analyze the sensitivity of our estimated metabolism rates to multiple environmental stressors. Results demonstrate that empirical models could be useful for rapid river health assessments, but need improvements to reproduce peak metabolism rates. The process‐based model, although more complex than existing in situ approaches to metabolism quantification, allows inference when gaps in continuous observations are present. The model offers additional benefits for predicting metabolism rates under future scenarios of environmental change incorporating multiple stressor effects.

СРАВНИТЕЛЬНЫЙ АНАЛИЗ ПОКАЗАТЕЛЕЙ ФИЗИЧЕСКОГО РАЗВИТИЯ И ФИЗИЧЕСКОЙ ПОДГОТОВЛЕННОСТИ ДЕВОЧЕК И МАЛЬЧИКОВ 7-8 ЛЕТ, ЗАНИМАЮЩИХСЯ ЛЕГКОЙ АТЛЕТИКОЙ

The research purpose is to conduct a comparative analysis of physical development and physical fitness indicators of 7-8 years old girls and boys engaged in athletics. Methods and organization of the research. To achieve the purpose, we used the following methods: analysis and generalization of scientific and methodological literature, anthropometry, integral assessment of physical health according to the method of S.V. Khrushchev, the method of functional tests, the method of control tests, methods of mathematical statistics. The research was conducted at the Sports School of Olympic reserve "Tasma" in Kazan. 57 children (37 boys and 20 girls) aged 7-8 years took part in it. The results of young athletes were compared with age norms of physical development and physical fitness indicators. The research results and their discussion. A total of 1,425 measurements were carried out. It was found that the indicators of physical development and physical fitness of the studied athletes aged 7-8 years old correspond to age norms. The average level of physical health was recorded in 75% of girls and 56.8% of boys. In terms of body length, systolic blood pressure, coordination abilities, boys at the age of 7-8 years old have significantly higher results compared to their female peers. At the same time, boys have lower indicators of flexibility. There were no statistically significant differences in other indicators of physical development and physical fitness, as well as in the rapid assessment of physical health. Conclusion. The results obtained confirm the presence of gender differences in the indicators of physical development and physical fitness of 7-8 years old athletes engaged in athletics. Accordingly, this should be taken into account when planning and organizing the training process with this contingent.

A decision‐tree‐based algorithm for identifying the extent of structural damage in braced‐frame buildings

Rapid health assessment of essential buildings such as hospitals, fire stations, and large residential complexes is crucial after damaging earthquakes. The use of advanced technologies such as wireless sensors, learning algorithms, and signal processing methods became more attractive in such fast applications due to their higher reliabilities and efficiencies compared to the conventional visual inspection methods. This paper presents a robust post-earthquake damage detection framework for predicting the extent and location of damage occurrence in the braced-frame structures after an earthquake. To do so, features derived from acceleration response of the structure were used along with a classification learner to determine the health condition of the structure. Decision tree classifiers are used for the purpose of damage classification where the Bayesian optimization algorithm is implemented to optimize the architecture of the mentioned classifier. A one-story chevron steel-braced frame, a three-story X-braced steel-frame, and a five-story three-dimensional building are considered to validate the proposed method. The total number of 3774 and 1887 nonlinear response history analyses were respectively performed for 2D and 3D numerical models under scaled SAC motions, using the OpenSees simulation platform. Furthermore, in order to simulate the field condition, a maximum level of 10% white Gaussian noise is added to the output signals. Results obtained from the three case studies show that the proposed framework is robust and reliable in predicting the extent of damage level in the braced-frame structures in a short time after an earthquake.

Rapid assessment of mental health of Detroit-area health care workers during the COVID-19 pandemic.

The COVID-19 pandemic may adversely impact the mental health of health care workers (HCWs). To address this issue, it is essential to determine levels of anxiety, depression, and traumatic stress, and sources of stress, and to identify subgroups of HCWs at a higher risk of adverse mental health outcomes during the COVID-19 pandemic. We conducted a cross-sectional study of symptoms of mental illness in HCWs in the area surrounding Detroit, Michigan. The online survey included questions about demographics, health and clinical factors, and sources of stress. Several tools were used to assess psychiatric symptoms among HCWs, including the Perceived Stress Scale, the Patient Health Questionnaire depression scale, the Generalized Anxiety Disorder 7-item assessment, and the Posttraumatic Stress Disorder Checklist for DSM-5. The adequacy of personal protective equipment, patient resources, and training for highly contagious diseases were rated. The sample (N = 129) was predominantly female (51.2%) and White (65.9%), with 30.2% screening positive for clinical follow-up to assess anxiety, 20.9% for moderate to severe depression, and 16.3% for elevated traumatic stress. Differences were found by self-reported psychiatric diagnosis and chronic conditions, and role on treatment teams. Frontline HCWs demonstrate high levels of stress and trauma symptoms. Timely screening and accommodations may be needed during health care crises, such as the COVID-19 pandemic.

Rapid Behavioral Health Assessment Post-disaster: Developing and Validating a Brief, Structured Module.

To develop and validate a brief, structured, behavioral health module for use by local public health practitioners to rapidly assess behavioral health needs in disaster settings. Data were collected through in-person, telephone, and web-based interviews of 101 individuals affected by Hurricanes Katrina (n = 44) and Sandy (n = 57) in New Orleans and New Jersey in April and May 2018, respectively. Questions included in the core module were selected based on convergent validity, internal consistency reliability, test–retest reliability across administration modes, principal component analysis (PCA), question comprehension, efficiency, accessibility, and use in population-based surveys. Almost all scales showed excellent internal consistency reliability (Cronbach’s alpha, 0.79–0.92), convergent validity (r > 0.61), and test–retest reliability (in-person vs. telephone, intra-class coefficient, ICC, 0.75–1.00; in-person vs. web-based ICC, 0.73–0.97). PCA of the behavioral health scales yielded two components to include in the module—mental health and substance use. The core module has 26 questions—including self-reported general health (1 question); symptoms of posttraumatic stress disorder, depression, and anxiety (Primary Care PTSD Screen, Patient Health Questionnaire-4; 8 questions); drinking and other substance use (Alcohol Use Disorders Identification Test-Concise, AUDIT-C; Drug Abuse Screening Test, DAST-10; stand-alone question regarding increased substance use since disaster; 14 questions); prior mental health conditions, treatment, and treatment disruption (3 questions)—and can be administered in 5–10 minutes through any mode. This flexible module allows practitioners to quickly evaluate behavioral health needs, effectively allocate resources, and appropriately target interventions to help promote recovery of disaster-affected communities.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10900-021-00966-5.

Kajian Indeks Resiko Bencana dengan Kesiapan Puskesmas dalam Managemen Bencana Kesehatan di Kota Jambi

This study aims to see the service system during a health disaster in the city of Jambi, namely Tahtul Yaman Puskesmas, Tanjung Pinang Health Center, and Aurduri Health Center. The short-term target is to find organizational standards and the role of the puskesmas team in disasters. The research method used survey analytics to assess the roles and functions that were involved in the health disaster management system and to analyze the readiness of 3 health centers in the city of Jambi. From the results of this study, according to the application of the Minister of Health Regulation number 75 of 2019 at the Tahtul Yaman Puskesmas, the Tanjung Pinang Health Center, and the first Aurduri Health Center, it is obtained from (1) There is no clear program regarding disaster management in each cluster at the Puskesmas, ( 2) Rapid Health Assessment (RHA) has not been implemented in the event of a disaster, (3) The coordination flow for the Puskesmas already exists, namely through referrals to the Abdul Manap Regional General Hospital, Jambi City and the Jambi City Health Office.

Paper chromatography: An inconsistent tool for assessing soil health

Although paper chromatography is being promoted as a cost-effective tool for rapid assessment of soil health, few studies have explored the quantitative relationship between chromatogram features and soil health variables, and no studies have investigated the association between chromatogram features and microbial diversity as determined using DNA sequences. We assessed 343 soil samples from varying land uses in southwestern Australia to investigate the relationship between total organic carbon, microbial activity and diversity, salinity and pH levels and 12 chromatogram features. Spearman’s correlations and variance partitioning were used to detect relationships. Although total organic carbon and microbial activity displayed the strongest correlations with chromatogram features, they were not associated with greater development of radial features and median zone radius as expected. This was in contrast to what has been previously reported, implying context dependent responses of chromatogram features to gradients in soil variables. Microbial community structure was found to explain changes in chromatogram features better than the measured soil variables. These results indicate that further studies are necessary before paper chromatography is embraced as a tool for soil health assessment, and raises questions regarding the use of chromatography by community groups as a tool to measure soil health.