Currently, the rising prevalence of resistant Candida species, particularly Candida albicans, as well as non-albicans isolates such as Candida glabrata and Candida krusei, represent challenges in their management. In this review, we aimed to explore the current management of fluconazole-resistant vulvovaginal candidiasis (FRVVC). Identified studies focused on alternative antifungal therapies, including boric acid, nystatin, and newer agents like oteseconazole and ibrexafungerp. The findings highlight the need for tailored treatment regimens, considering the variability in resistance patterns across regions. Unprofessional as well as professional overuse of antifungals for vulvovaginal symptoms that are not caused by Candida infections should be combatted and banned as much as possible. Instead of high-dose maintenance regimens using weekly doses of 150 to 200 mg of fluconazole for 6 months or longer, it is advisable to use an individualised degressive regimen (ReCiDiF regimen) in order to tailor the treatment of a particular patient to the lowest dosage possible to keep the diseases controlled. Additionally, this report underscores the impact of antibiotic use on the microbiota, which can raise the possibility of VVC and lead to fluconazole resistance, emphasizing the necessity for cautious antibiotic prescribing practices.

Long COVID or Post-Acute Sequelae of SARS-CoV-2 infection (PASC) is a condition characterized by numerous lingering symptoms that persist for weeks to months following the viral illness. While treatment for PASC is still evolving, several therapeutic approaches beyond traditional antiviral therapies are being investigated, such as immune-modulating agents, anti-inflammatory drugs, and various supportive interventions focusing at alleviating symptoms and enhancing recovery. We aimed to summarize the breadth of available evidence, identify knowledge gaps, and highlight promising non-antiviral therapies for Long COVID/PASC. We followed the framework of a scoping methodology by mapping existing evidence from a range of studies, including randomized clinical trials, observational research, and case series. Treatments evaluated include metformin, low-dose naltrexone (LDN), dexamethasone, statins, omega-3 fatty acids, L-arginine, and emerging therapies like intravenous immunoglobulin (IVIg) and therapeutic apheresis. Early findings suggest that metformin has the strongest clinical evidence, particularly from large phase 3 trials, while LDN and dexamethasone show potential based on observational studies. However, many treatments lack robust, large-scale trials. This review emphasizes the need for further research to confirm the efficacy of these treatments and guide clinical practice for Long COVID management.

Cellular and humoral immunity exhibit dynamic adaptation to the mutating SARS-CoV-2 virus. It is noteworthy that immune responses differ significantly, influenced by whether a patient has received vaccination or whether there is co-occurrence of naturally acquired and vaccine-induced immunity, known as hybrid immunity. The different immune reactions, conditional on vaccination status and the viral variant involved, bear implications for inflammatory responses, patient outcomes, pathogen transmission rates, and lingering post-COVID conditions. Considering these developments, we have performed a review of recently published literature, aiming to disentangle the intricate relationships among immunological profiles, transmission, the long-term health effects post-COVID infection poses, and the resultant clinical manifestations. This investigation is directed toward understanding the variability in the longevity and potency of cellular and humoral immune responses elicited by immunization and hybrid infection.

Chronic fatigue syndrome (CFS) remains a subject of scientific research specifically with regards to its association with infections, including the more recently described Long COVID condition. Chronic fatigue and sleep disturbances in Long COVID are intricately linked to disruptions in circadian rhythms, driven by distinct molecular and cellular mechanisms triggered by SARS-CoV-2 infection. This can be driven by various mechanisms including dysregulation of key clock genes (CLOCK, BMAL1, PER2), mitochondrial dysfunction impairing oxidative phosphorylation, and cytokine-induced neuroinflammation (e.g., interleukin-6, tumor necrosis factor-alpha). Epigenetic changes, including DNA methylation at clock-related loci, particularly in peripheral tissues, further contribute to systemic circadian dysregulation. This work underscores the multifaceted molecular and systemic disruptions to circadian regulation in relation to fatigue and sleep disturbances identified as post-infectious sequelae, focusing on the Long COVID condition.

The ongoing COVID-19 pandemic has significantly impacted special populations, including immunocompromised individuals, people living with HIV (PLWHIV), pediatric patients, and those with chronic liver disease (CLD). This scoping review aims to map the clinical outcomes of these vulnerable groups when infected with various SARS-CoV-2 variants. The review identifies trends and patterns, noting that early variants, such as Alpha and Delta, are associated with more severe outcomes, including higher hospitalization and mortality rates. In contrast, the Omicron variant, despite its increased transmissibility, tends to cause milder clinical manifestations. The review highlights the necessity for ongoing surveillance and tailored healthcare interventions due to the heterogeneity of patient populations and the evolving nature of the virus. Continuous monitoring and adaptive healthcare strategies are essential to mitigate the impact of COVID-19 on these high-risk groups.

Artificial intelligence (AI) techniques—ranging from hybrid mechanistic–machine learning (ML) ensembles to gradient-boosted decision trees, support-vector machines, and deep neural networks—are transforming the management of seasonal influenza, respiratory syncytial virus (RSV), human immunodeficiency virus (HIV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Symptom-based triage models using eXtreme Gradient Boosting (XGBoost) and Random Forests, as well as imaging classifiers built on convolutional neural networks (CNNs), have improved diagnostic accuracy across respiratory infections. Transformer-based architectures and social media surveillance pipelines have enabled real-time monitoring of COVID-19. In HIV research, support-vector machines (SVMs), logistic regression, and deep neural network (DNN) frameworks advance viral-protein classification and drug-resistance mapping, accelerating antiviral and vaccine discovery. Despite these successes, persistent challenges remain—data heterogeneity, limited model interpretability, hallucinations in large language models (LLMs), and infrastructure gaps in low-resource settings. We recommend standardized open-access data pipelines and integration of explainable-AI methodologies to ensure safe, equitable deployment of AI-driven interventions in future viral-outbreak responses.