Source-Sink Dynamics in a Two-Patch SI Epidemic Model with Life Stages and No Recovery from Infection.

This study presents a comprehensive analysis of a two-patch, two-life stage SI model without recovery from infection, focusing on the dynamics of disease spread and host population viability in natural populations. The model, inspired by real-world ecological crises like the decline of amphibian populations due to chytridiomycosis and sea star populations due to Sea Star Wasting Disease, aims to understand the conditions under which a sink host population can present ecological rescue from a healthier, source population. Mathematical and numerical analyses reveal the critical roles of the basic reproductive numbers of the source and sink populations, the maturation rate, and the dispersal rate of juveniles in determining population outcomes. The study identifies basic reproduction numbers R 0 for each of the patches, and conditions for the basic reproduction numbers to produce a receiving patch under which its population. These findings provide insights into managing natural populations affected by disease, with implications for conservation strategies, such as the importance of maintaining reproductively viable refuge populations and considering the effects of dispersal and maturation rates on population recovery. The research underscores the complexity of host-pathogen dynamics in spatially structured environments and highlights the need for multi-faceted approaches to biodiversity conservation in the face of emerging diseases.

The effect of preoperative clindamycin in reducing early oral implant failure: a randomised placebo-controlled clinical trial.

OBJECTIVES: To assess the effect of preoperative oral clindamycin in reducing early implant failure in healthy adults undergoing conventional implant placement. MATERIALS AND METHODS: We conducted a prospective, randomised, double-blind, placebo-controlled clinical trial in accordance with the ethical principles and Consolidated Standards of Reporting Trials statement. We included healthy adults who underwent a single oral implant without previous infection of the surgical bed or the need for bone grafting. They were randomly treated with a single dose of oral clindamycin (600 mg) 1 h before surgery or a placebo. All surgical procedures were performed by one surgeon. A single trained observer evaluated all patients on postoperative days 1, 7, 14, 28, and 56. Early dental implant failure was defined as the loss or removal of an implant for any reason. We recorded the clinical, radiological, and surgical variables, adverse events, and postoperative complications. The study outcomes were statistically analysed to evaluate differences between the groups. Furthermore, we calculated the number required to treat or harm (NNT/NNH). RESULTS: Both the control group and clindamycin group had 31 patients each. Two implant failures occurred in the clindamycin group (NNH = 15, p = 0.246). Three patients had postoperative infections, namely two placebo-treated and one clindamycin-treated, which failed (relative risk: 0.5, CI: 0.05-5.23, absolute risk reduction = 0.03, confidence interval: - 0.07-0.13, NNT = 31, CI: 7.2-∞, and p = 0.5). One clindamycin-treated patient experienced gastrointestinal disturbances and diarrhoea. CONCLUSIONS: Preoperative clindamycin administration during oral implant surgery in healthy adults may not reduce implant failure or post-surgical-complications. CLINICAL RELEVANCE: Oral clindamycin is not efficacy. TRIAL REGISTRATION: The present trial was registered (EudraCT number: 2017-002,168-42). It was approved by the Committee for the Ethics of Research with Medicines of Euskadi (CEIm-E) on 31 October 2018 (internal code number: 201862) and the Spanish Agency of Medicines and Medical Devices (AEMPS) on 18 December 2018.

Is clindamycin effective in preventing infectious complications after oral surgery? Systematic review and meta-analysis of randomized controlled trials.

OBJECTIVE: To determine the effect of clindamycin in the prevention of infection after oral surgery. MATERIAL AND METHODS: This systematic review and meta-analysis followed the PRISMA statement, the PICO-framework and included only randomized controlled clinical trials. In all studies clindamycin was administered to prevent infections in patients who underwent oral surgery. Two independent researchers conducted the search, data extraction and risk of bias assessment. Included studies were classified by the type of oral surgery. Besides, data of patients, procedures and outcome variables were collected. Risk ratios (RR) and 95% confidence intervals (CI) were calculated by using Mantel-Haenszel model and the number needed to treat (NNT). Finally, any potential sources of heterogeneity were estimated. RESULTS: Seven trials of 540 articles met the inclusion criteria and were included in the qualitative synthesis. Four articles assessing the effect of oral clindamycin in third molar surgery were quantitatively analyzed. The overall RR was 0.66 (95% CI = 0.38-1.16), being non-statistically significant (p = 0.15). There was no heterogeneity between the studies I2 = 0, p = 0.44. The NNT was 29 (95% CI = 12- -57). CONCLUSIONS: The effectiveness of clindamycin could not be evaluated except in third molar extraction. Oral clindamycin is ineffective in preventing infection in third molar surgery. CLINICAL RELEVANCE: There is a lack of high-quality evidence supporting the prescription of clindamycin to prevent infections after oral surgery, despite being frequently prescribed as an alternative for penicillin-allergic patients. Oral clindamycin has not been shown to be effective after third molar extractions.

Mathematical and statistical models for the control of mosquito-borne diseases: the experience of Costa Rica.

Objective: To summarize the results of research conducted in Costa Rica in which mathematical and statistical methods were implemented to study the transmission dynamics of mosquito-borne diseases. Methods: Three articles with mathematical and statistical analysis on vector-borne diseases in Costa Rica were selected and reviewed. These papers show the value and relevance of using different quantitative methods to understand disease dynamics and support decision-making. Results: The results of these investigations: 1) show the impact on dengue case reports when a second pathogen emerges, such as chikungunya; 2) recover key parameters in Zika dynamics using Bayesian inference; and 3) show the use of machine learning algorithms and climatic variables to forecast the dengue relative risk in five different locations. Conclusions: Mathematical and statistical modeling enables the description of mosquito-borne disease transmission dynamics, providing quantitative information to support prevention/control methods and resource allocation planning.

Genome-resolved metagenome and metatranscriptome analyses of thermophilic composting reveal key bacterial players and their metabolic interactions.

BACKGROUND: Composting is an important technique for environment-friendly degradation of organic material, and is a microbe-driven process. Previous metagenomic studies of composting have presented a general description of the taxonomic and functional diversity of its microbial populations, but they have lacked more specific information on the key organisms that are active during the process. RESULTS: Here we present and analyze 60 mostly high-quality metagenome-assembled genomes (MAGs) recovered from time-series samples of two thermophilic composting cells, of which 47 are potentially new bacterial species; 24 of those did not have any hits in two public MAG datasets at the 95% average nucleotide identity level. Analyses of gene content and expressed functions based on metatranscriptome data for one of the cells grouped the MAGs in three clusters along the 99-day composting process. By applying metabolic modeling methods, we were able to predict metabolic dependencies between MAGs. These models indicate the importance of coadjuvant bacteria that do not carry out lignocellulose degradation but may contribute to the management of reactive oxygen species and with enzymes that increase bioenergetic efficiency in composting, such as hydrogenases and N2O reductase. Strong metabolic dependencies predicted between MAGs revealed key interactions relying on exchange of H+, NH3, O2 and CO2, as well as glucose, glutamate, succinate, fumarate and others, highlighting the importance of functional stratification and syntrophic interactions during biomass conversion. Our model includes 22 out of 49 MAGs recovered from one composting cell data. Based on this model we highlight that Rhodothermus marinus, Thermobispora bispora and a novel Gammaproteobacterium are dominant players in chemolithotrophic metabolism and cross-feeding interactions. CONCLUSIONS: The results obtained expand our knowledge of the taxonomic and functional diversity of composting bacteria and provide a model of their dynamic metabolic interactions.

Antibiotic prophylaxis prescribing habits in oral implant surgery in the Netherlands: a cross-sectional survey.

BACKGROUND: There seems to be no consensus on the prescription of prophylactic antibiotics in oral implant surgery. The Dutch Association of Oral Implantology (NVOI) guidelines do not include a clear policy on prophylactic antibiotic prescriptions for oral implant surgery among healthy patients. The purpose of the study was to determine whether antibiotic prophylaxis is commonly prescribed in the Netherlands by general dentists, maxillofacial surgeons and oral implantologists in conjunction with oral implant surgery among healthy patients and to assess the type and amount of prophylactic antibiotic prescribed. METHODS: This observational cross-sectional study is based on a web survey. A questionnaire developed in the United States of America was translated and slightly adjusted for use in the Netherlands. It contained predominantly close-ended questions relating to demographics, qualifications, antibiotic type, prescription duration and dosage. An email including an introduction to the study and an individual link to the questionnaire was sent in February 2018 to a sample of 600 general dental practitioners and all 302 specialized dentists (oral implantologists, periodontists and maxillofacial surgeons) recognized by the NVOI. Overall, 902 questionnaires were anonymously sent. Finally, 874 potential participants were reached. Collected data were analyzed through descriptive statistics. RESULTS: In total, 218 (24.9%) participants responded to the questionnaire, including 45 females (20.8%) and 171 males (79.2%). Overall, 151 (69.9%) regularly placed oral implants. Of them, 79 (52.7%) prescribe antibiotics only in specific situations, 66 (43.7%) regularly, and 5 (3.3%) did not prescribe antibiotics at all. Overall, 83 participants who prescribe antibiotics did so both pre- and postoperatively (57.2%), 47 only preoperatively (32.4%) and 12 exclusively postoperatively (8.3%). A single dose of 2000 mg of amoxicillin orally one hour prior to surgery was the most prescribed preoperative regimen. The most frequently prescribed postoperative regimen was 500 mg of amoxicillin three times daily for five days after surgery. On average, participants prescribe a total of 7018 mg of antibiotics before, during or after oral implant surgery. CONCLUSIONS: Antibiotic prophylaxis in conjunction with oral implant surgery is prescribed in the Netherlands on a large scale, and recommendations based on the last published evidence are frequently not followed.

Antibiotic prophylaxis habits in oral implant surgery among dentists in Italy: a cross-sectional survey.

BACKGROUND: The prescription of prophylactic antibiotics in conjunction with oral implant surgery remains inconsistent among different populations of dentists. The main objective of this study was to assess the current antibiotic prescribing habits of dentist in conjunction with oral implant surgery in Italy. The secondary objective was to assess the nature and amount (mg) of antibiotics prescriptions in order to evaluate whether any consensus has been reached and if the current recommendations are complied. METHODS: Observational cross-sectional study based on a web-survey reported according to the STROBE guidelines. A questionnaire was sent via email to each registered member of the Italian Academy of Osseointegration (n = 400). The email included a link to the anonym web questionnaire developed on www.encuestafacil.com. It contained close-ended and some open-ended questions concerning demographics, antibiotic type, prescription duration and dosage. Collected data were analyzed using STATA® 14 software. RESULTS: 160 participants responded the survey (response rate = 40%). Approximately 84% routinely prescribed prophylactic antibiotics in conjunction with oral implant surgery, 15.6% prescribed antibiotics in certain situations and only 1 did not prescribe antibiotics at all. Overall, 116 respondents prescribed both pre- and postoperative antibiotics, 29 prescribed antibiotics only preoperatively and 14 prescribed antibiotics exclusively after surgery. Italian dentists prescribed an average amount of 10,331 mg antibiotics before, during or after oral implant surgery. Approximately, only 17% (n = 27) of the participants who prescribed antibiotics before oral implant surgery complied with the recommendations proposed by the latest publications (no more than 3 g of preoperative amoxicillin before oral implant surgery). CONCLUSIONS: Dentists in Italy on a large scale prescribe antibiotic prophylaxis in conjunction with oral implant surgery among healthy patients. A high range of prophylactic regimens is prescribed and they are not adhering to the new science-based specifications. Guidelines focused on the indications for prophylactic antibiotics among healthy patients are required to prevent bacterial resistance, side effects and costs caused by overtreatment and the irrational use of antibiotics.

Does Chlorhexidine Prevent Alveolar Osteitis After Third Molar Extractions? Systematic Review and Meta-Analysis.

PURPOSE: The prevention of alveolar osteitis (AO) in dental extractions remains a controversial issue. Chlorhexidine is one of the most widely studied antiseptics for the prevention of AO. The purpose of this systematic review and meta-analysis was to assess the efficacy and effectiveness of chlorhexidine in the prevention of AO after third molar extractions. MATERIALS AND METHODS: The authors searched databases and the references of each article retrieved up to December 2015. Clinical randomized controlled trials (RCTs) using only chlorhexidine were included. The predictor variable was whether chlorhexidine was used in any formulation, concentration, or regimen. The outcome measurement was the incidence of postoperative AO. The authors also recorded variables describing the characteristics of the included studies. Statistical analysis was performed using STATA 12.0. Meta-analysis of binary data was conducted using a fixed-effects model. Risk ratios and 95% confidence intervals (CIs) were estimated. Forest, l'Abbé, and funnel plots were constructed. RESULTS: Twenty-three studies published from 1979 to 2015, corresponding to 18 trials (16 parallel-group and 2 split-mouth RCTs), that reported on 2,824 third molar extractions (1,458 in experimental group and 1,366 in control group) were included. The overall relative risk (RR) was 0.53 (95% CI, 0.45-0.62; P < .0001). There was no evidence of heterogeneity (I2 = 9.3%; P = .336 by χ2 test). The number needed to treat was 8 (95% CI, 7-11). There were no relevant differences between chlorhexidine rinse (RR = 0.58; 95% CI, 0.47-0.71) and gel (RR = 0.47; 95% CI, 0.37-0.60). Chlorhexidine did not cause a larger proportion of adverse reactions than placebo. CONCLUSION: The use of chlorhexidine, in any formulation, concentration, or regimen, is efficacious and effective in preventing AO in patients who have undergone third molar extraction. Chlorhexidine gel was found to be moderately more efficacious than the rinse formulation.

How to Obtain and Compare Metagenome-Assembled Genomes.

Metagenome-assembled genomes, or MAGs, are genomes retrieved from metagenome datasets. In the vast majority of cases, MAGs are genomes from prokaryotic species that have not been isolated or cultivated in the lab. They, therefore, provide us with information on these species that are impossible to obtain otherwise, at least until new cultivation methods are devised. Thanks to improvements and cost reductions of DNA sequencing technologies and growing interest in microbial ecology, the rise in number of MAGs in genome repositories has been exponential. This chapter covers the basics of MAG retrieval and processing and provides a practical step-by-step guide using a real dataset and state-of-the-art tools for MAG analysis and comparison.

Global proteome of the saprophytic strain Leptospira biflexa and comparative analysis with pathogenic strain Leptospira interrogans uncover new pathogenesis mechanisms.

Leptospira is a genus of bacteria that includes free-living saprophytic species found in water or soil, and pathogenic species, which are the etiologic agents of leptospirosis. Besides all the efforts, there are only a few proteins described as virulence factors in the pathogenic strain L. interrogans. This work aims to perform L. biflexa serovar Patoc1 strain Paris global proteome and to compare with the proteome database of pathogenic L. interrogans serovar Copenhageni strain Fiocruz L1-130. We identified a total of 2327 expressed proteins of L. biflexa by mass spectrometry. Using the Get Homologues software with the global proteome of L. biflexa and L. interrogans, we found orthologous proteins classified into conserved, low conserved, and specific proteins. Comparative bioinformatic analyses were performed to understand the biological functions of the proteins, subcellular localization, the presence of signal peptide, structural domains, and motifs using public softwares. These results lead to the selection of 182 low conserved within the saprophyte, and 176 specific proteins of L. interrogans. It is anticipated that these findings will indicate further studies to uncover virulence factors in the pathogenic strain. This work presents for the first time the global proteome of saprophytic strain L. biflexa serovar Patoc, strain Patoc1. SIGNIFICANCE: The comparative analysis established an array of specific proteins in pathogenic strain that will narrow down the identification of immune protective proteins that will help fight leptospirosis.

Diabetes current and future translatable therapies.

Diabetes is one of the major diseases and concerns of public health systems that affects over 200 million patients worldwide. It is estimated that 90% of these patients suffer from diabetes type 2, while 10% present diabetes type 1. This type of diabetes and certain types of diabetes type 2, are characterized by dysregulation of blood glycemic levels due to the total or partial depletion of insulin-secreting pancreatic ß-cells. Different approaches have been proposed for long-term treatment of insulin-dependent patients; amongst them, cell-based approaches have been the subject of basic and clinical research since they allow blood glucose level sensing and in situ insulin secretion. The current gold standard for insulin-dependent patients is on-demand exogenous insulin application; cell-based therapies aim to remove this burden from the patient and caregivers. In recent years, protocols to isolate and implant pancreatic islets from diseased donors have been developed and tested in clinical trials. Nevertheless, the shortage of donors, along with the need of immunosuppressive companion therapies, have pushed researchers to focus their attention and efforts to overcome these disadvantages and develop alternative strategies. This review discusses current tested clinical approaches and future potential alternatives for diabetes type 1, and some diabetes type 2, insulin-dependent patients. Additionally, advantages and disadvantages of these discussed methods.

Osteomyelitis is associated with increased anti-inflammatory response and immune exhaustion.

Introduction: Osteomyelitis (OMS) is a bone infection causing bone pain and severe complications. A balanced immune response is critical to eradicate infection without harming the host, yet pathogens manipulate immunity to establish a chronic infection. Understanding OMS-driven inflammation is essential for disease management, but comprehensive data on immune profiles and immune cell activation during OMS are lacking. Methods: Using high-dimensional flow cytometry, we investigated the detailed innate and adaptive systemic immune cell populations in OMS and age- and sex-matched controls. Results: Our study revealed that OMS is associated with increased levels of immune regulatory cells, namely T regulatory cells, B regulatory cells, and T follicular regulatory cells. In addition, the expression of immune activation markers HLA-DR and CD86 was decreased in OMS, while the expression of immune exhaustion markers TIM-3, PD-1, PD-L1, and VISTA was increased. Members of the T follicular helper (Tfh) cell family as well as classical and typical memory B cells were significantly increased in OMS individuals. We also found a strong correlation between memory B cells and Tfh cells. Discussion: We conclude that OMS skews the host immune system towards the immunomodulatory arm and that the Tfh memory B cell axis is evident in OMS. Therefore, immune-directed therapies may be a promising alternative for eradication and recurrence of infection in OMS, particularly in individuals and areas where antibiotic resistance is a major concern.

A mathematical model with nonlinear relapse: conditions for a forward-backward bifurcation.

We constructed a Susceptible-Addicted-Reformed model and explored the dynamics of nonlinear relapse in the Reformed population. The transition from susceptible considered at-risk is modeled using a strictly decreasing general function, mimicking an influential factor that reduces the flow into the addicted class. The basic reproductive number is computed, which determines the local asymptotically stability of the addicted-free equilibrium. Conditions for a forward-backward bifurcation were established using the basic reproductive number and other threshold quantities. A stochastic version of the model is presented, and some numerical examples are shown. Results showed that the influence of the temporarily reformed individuals is highly sensitive to the initial addicted population.

A nonlinear relapse model with disaggregated contact rates: Analysis of a forward-backward bifurcation.

Throughout the progress of epidemic scenarios, individuals in different health classes are expected to have different average daily contact behavior. This contact heterogeneity has been studied in recent adaptive models and allows us to capture the inherent differences across health statuses better. Diseases with reinfection bring out more complex scenarios and offer an important application to consider contact disaggregation. Therefore, we developed a nonlinear differential equation model to explore the dynamics of relapse phenomena and contact differences across health statuses. Our incidence rate function is formulated, taking inspiration from recent adaptive algorithms. It incorporates contact behavior for individuals in each health class. We use constant contact rates at each health status for our analytical results and prove conditions for different forward-backward bifurcation scenarios. The relationship between the different contact rates heavily influences these conditions. Numerical examples highlight the effect of temporarily recovered individuals and initial conditions on infected population persistence.

An implementation of a multilayer network model for the Covid-19 pandemic: A Costa Rica study.

We present a numerical implementation for a multilayer network to model the transmission of Covid-19 or other diseases with a similar transmission mechanism. The model incorporates different contact types between individuals (household, social and sporadic networks) and includes an SEIR type model for the transmission of the virus. The algorithm described in this paper includes the main ideas of the model used to give public health authorities an additional tool for the decision-making process in Costa Rica by simulating extensive possible scenarios and projections. We include two simulations: a study of the effect of restrictions on the transmission of the virus and a Costa Rica case study that was shared with the Costa Rican health authorities.

Assessing dengue fever risk in Costa Rica by using climate variables and machine learning techniques.

Dengue fever is a vector-borne disease affecting millions yearly, mostly in tropical and subtropical countries. Driven mainly by social and environmental factors, dengue incidence and geographical expansion have increased in recent decades. Therefore, understanding how climate variables drive dengue outbreaks is challenging and a problem of interest for decision-makers that could aid in improving surveillance and resource allocation. Here, we explore the effect of climate variables on relative dengue risk in 32 cantons of interest for public health authorities in Costa Rica. Relative dengue risk is forecast using a Generalized Additive Model for location, scale, and shape and a Random Forest approach. Models use a training period from 2000 to 2020 and predicted climatic variables obtained with a vector auto-regressive model. Results show reliable projections, and climate variables predictions allow for a prospective instead of a retrospective study.

Digital Platform for Continuous Monitoring of Patients Using a Smartwatch: Longitudinal Prospective Cohort Study.

BACKGROUND: Since the COVID-19 pandemic, there has been a boost in the digital transformation of the human society, where wearable devices such as a smartwatch can already measure vital signs in a continuous and naturalistic way; however, the security and privacy of personal data is a challenge to expanding the use of these data by health professionals in clinical follow-up for decision-making. Similar to the European General Data Protection Regulation, in Brazil, the Lei Geral de Proteção de Dados established rules and guidelines for the processing of personal data, including those used for patient care, such as those captured by smartwatches. Thus, in any telemonitoring scenario, there is a need to comply with rules and regulations, making this issue a challenge to overcome. OBJECTIVE: This study aimed to build a digital solution model for capturing data from wearable devices and making them available in a safe and agile manner for clinical and research use, following current laws. METHODS: A functional model was built following the Brazilian Lei Geral de Proteção de Dados (2018), where data captured by smartwatches can be transmitted anonymously over the Internet of Things and be identified later within the hospital. A total of 80 volunteers were selected for a 24-week follow-up clinical trial divided into 2 groups, one group with a previous diagnosis of COVID-19 and a control group without a previous diagnosis of COVID-19, to measure the synchronization rate of the platform with the devices and the accuracy and precision of the smartwatch in out-of-hospital conditions to simulate remote monitoring at home. RESULTS: In a 35-week clinical trial, >11.2 million records were collected with no system downtime; 66% of continuous beats per minute were synchronized within 24 hours (79% within 2 days and 91% within a week). In the limit of agreement analysis, the mean differences in oxygen saturation, diastolic blood pressure, systolic blood pressure, and heart rate were -1.280% (SD 5.679%), -1.399 (SD 19.112) mm Hg, -1.536 (SD 24.244) mm Hg, and 0.566 (SD 3.114) beats per minute, respectively. Furthermore, there was no difference in the 2 study groups in terms of data analysis (neither using the smartwatch nor the gold-standard devices), but it is worth mentioning that all volunteers in the COVID-19 group were already cured of the infection and were highly functional in their daily work life. CONCLUSIONS: On the basis of the results obtained, considering the validation conditions of accuracy and precision and simulating an extrahospital use environment, the functional model built in this study is capable of capturing data from the smartwatch and anonymously providing it to health care services, where they can be treated according to the legislation and be used to support clinical decisions during remote monitoring.

Mesenchymal Stem Cells: Case Report of an Adjuvant Ambulatory Therapy for a COVID-19 High-Risk and Steroid-hypersensitive Patients.

INTRODUCTION: Due to the rapid progression of COVID-19 to severe and critical stages, thousands of patients have required the use of intensive care unit (ICU) treatment, placing an excessive strain on health systems. Immunomodulatory effects of Wharton's Jelly Mesenchymal Stem Cells (WJ-MSCs) have shown promising results in the treatment of patients with COVID-19. However, the effect of promptly applied cell therapy on ambulatory patient prognosis has not been described. This case report presents the clinical outcome of a multimorbid, steroid-hypersensitive, COVID-19 patient treated with WJ-MSCs transplantation. CASE PRESENTATION: A 67-year-old woman with Type 2 diabetes, overweight (82 kg, 168 cm, BMI = 29.053), hypertension (190/60 mmHg) and steroid-hypersensitivity, tested positive for COVID-19 after presenting typical symptoms such as fatigue, chest pain, myalgia, nasal congestion, dysgeusia, anosmia and oxygen saturation (SpO2) 94% - 96%, with normal body temperature (36°C). The patient received pharmacologic treatment but, when symptoms worsened, WJ-MSCs were transplanted to modulate the suspected onset of the cytokine release syndrome. Significant improvement of symptoms and clinical parameters (inflammatory markers and CT score) was observed, and the patient fully recovered within a short period of time. CONCLUSION: The present case report exhibits the favorable outcome of using Wharton's Jelly Mesenchymal Stem Cells (WJ-MSCs) as an ambulatory and adjuvant therapy for COVID-19. Prompt WJ-MSCs infusion can be a safe ambulatory adjuvant therapy in COVID-19 infection care, preventing disease progression to critical stages and avoiding hospital overcrowding.

Common patterns between dengue cases, climate, and local environmental variables in Costa Rica: A wavelet approach.

Dengue transmission poses significant challenges for public health authorities worldwide due to its susceptibility to various factors, including environmental and climate variability, affecting its incidence and geographic spread. This study focuses on Costa Rica, a country characterized by diverse microclimates nearby, where dengue has been endemic since its introduction in 1993. Using wavelet coherence and clustering analysis, we performed a time-series analysis to uncover the intricate connections between climate, local environmental factors, and dengue occurrences. The findings indicate that multiannual dengue frequency (3 yr) is correlated with the Oceanic Niño Index and the Tropical North Atlantic Index. This association is particularly prominent in cantons located along the North and South Pacific Coast, as well as in the Central cantons of the country. Furthermore, the time series of these climate indices exhibit a leading phase of approximately nine months ahead of dengue cases. Additionally, the clustering analysis uncovers non-contiguous groups of cantons that exhibit similar correlation patterns, irrespective of their proximity or adjacency. This highlights the significance of climate factors in influencing dengue dynamics across diverse regions, regardless of spatial closeness or distance between them. On the other hand, the annual dengue frequency was correlated with local environmental indices. A persistent correlation between dengue cases and local environmental variables is observed over time in the North Pacific and the Central Region of the country's Northwest, with environmental factors leading by less than three months. These findings contribute to understanding dengue transmission's spatial and temporal dynamics in Costa Rica, highlighting the importance of climate and local environmental factors in dengue surveillance and control efforts.

Value of Diagnostic Tools in the Diagnosis of Osteomyelitis: Pilot Study to Establish an Osteomyelitis Score.

Osteomyelitis (OM) remains one of the most feared complications in bone surgery and trauma. Its diagnosis remains a major challenge due to lack of guidelines. The aim of this study was to prospectively analyze the value of the most common and available diagnostic tools and to establish an OM score to derive treatment recommendations. All patients with suspected OM were included in a prospective pilot study. All patients underwent blood sampling for C-reactive protein and white blood cell count analysis. Magnetic resonance imaging (MRI), and microbiologic and histopathologic samples, were taken from representative sites of initial debridement. All patients were treated according to their OM test results and followed for at least one year. Subsequently, the value of individual or combined diagnostic tools was analyzed in patients with confirmed OM and in patients in whom OM was ruled out. Based on these findings, an OM score was developed that included MRI, microbiology, and histopathology. The score identified all control patients and all but one OM patient, resulting in a correct diagnosis of 93.3%, which was validated in a second independent larger cohort. This was the first study to analyze the value of the most commonly used tools to diagnose OM. The proposed OM score provides a simple scoring system to safely interpret test results with high accuracy.