Background and Objectives: Consequences due to infection with SARS-CoV-2 virus can have a direct impact on skeletal muscle, due to the fact that both cardiac and skeletal muscle tissue show robust ACE2(angiotensin-converting enzyme 2) expression, suggesting a potential susceptibility to SARS-CoV-2 infection in both types of tissues. From the articles analyzed we concluded that the musculoskeletal damage is firstly produced by the inflammatory effects, cytokine storm and muscle catabolism. However, myopathy, polyneuropathy and therapies such as corticoids were also considered important factors in muscle fatigue and functional incapacity. Pulmonary rehabilitation programs and early mobilization had a highly contribution during the acute phase and post-illness recovery process and helped patients to reduce dyspnea, increase the capacity of physical effort, overcome psychological disorders and improved the quality of their life. Materials and Methods: We have included in this review 33 articles that contain data on muscle damage following SARS-CoV-2 infection. We used the following keywords to search for articles: SARS-CoV-2, COVID-19, muscle weakness, muscle disease, muscle fatigue, neurological disorders. As a search strategy we used PubMed, Cochrane Database of Systematic Reviews; Database of Abstracts of Reviews of Effects and Health Technology Assessment Database to collect the information. We also have chosen the most recent articles published in the last 5 years. Conclusions: Muscular damage, as well as the decrease in the quality of life, are often a consequence of severe SARS-CoV-2 infection through: systemic inflammation, corticotherapy, prolonged bed rest and other unknown factors. Pulmonary rehabilitation programs and early mobilization had a highly contribution during the acute phase and post-illness recovery process and helped patients to reduce dyspnea, increase the capacity of physical effort, overcome psychological disorders and improve the quality of their life.

The acute phase of COVID-19 often leaves patients with persistent pulmonary deficits. Pulmonary Rehabilitation (PR) has been recommended as an essential part of post-acute COVID-19 management. In light of the global pandemic, telerehabilitation has been increasingly employed to deliver PR. This systematic review aimed to evaluate the effectiveness of telemedicine-driven PR in patients recovering from the acute phase of COVID-19, assessing variations in telerehabilitation practices and identifying the degree of change in mental health, physical health, quality of life, and lung function. A systematic search was conducted across PubMed, Web of Science, Cochrane, and Scopus up until April 2023. Studies focusing on telerehabilitation in PR for post-acute COVID-19 patients with outcomes including pulmonary function, exercise capacity, and quality of life were included after careful assessment of this study’s protocol. The selection process involved careful scrutiny of abstracts and full texts, and the quality assessment was performed using the National Heart, Lung, and Blood Institute (NHLBI) tool. Seven studies, published between 2021 and 2022, involving a total of 412 patients, were included. The evaluated telerehabilitation programs stretched between 4 and 10 weeks, involving a mobile app or video connection with the patient, integrating a mix of aerobic and resistance training, breathing exercises, functional activities, and muscle strengthening. Findings revealed that telemedicine-driven PR significantly improved physical health, measured by the step test score (73 vs. 71), 6MWD (30.2 vs. 17.1) and BPAQ, mental health evaluated by SF-12 (6.15 vs. 4.17) and PHQ-4, quality of life measured by the SF-12 (7.81 vs. 3.84), SGRQ (31.5 vs. 16.9), and CAT scores, and some parameters of pulmonary function in post-acute COVID-19 patients (mMRC, STST, and MVV). This review substantiates the potential of telemedicine-driven PR to improve various health outcomes in post-acute COVID-19 patients. The findings underscore the importance of integrating telerehabilitation into the management of post-acute COVID-19 and call for further exploration of its long-term effects, cost-effectiveness, and best practices.

Asthma is a prevalent chronic disease, contributing significantly to the global burden of disease and economic costs. Despite advances in treatment, inadequate disease management and reliance on reliever medications lead to preventable deaths. Telemedicine, defined as the use of information and communication technology to improve healthcare access, has gained global attention, especially during the COVID-19 pandemic. This systematic review examines the effectiveness of home monitoring systems in managing severe asthma. A systematic literature search was conducted in PubMed, Web of Science, Scopus, and Cochrane Library, focusing on studies from 2014 to 2024. Fourteen studies involving 9093 patients were analyzed. The results indicate that telemedicine, through tools such as mobile applications and portable spirometers, positively impacts asthma control, self-management, and quality of life. Home spirometry, in particular, shows strong agreement with clinic spirometry, offering a feasible alternative for continuous monitoring. Digital coaching and machine learning-based telemedicine applications also demonstrate significant potential in improving asthma outcomes. However, challenges such as technology accessibility, data privacy, and the need for standardized protocols remain. This review highlights the promise of telemedicine in asthma management and calls for further research to optimize its implementation and address existing barriers.

The High-Resolution Computed Tomography (HRCT) detection and diagnosis of diffuse lung disease is primarily based on the recognition of a limited number of specific abnormal findings, pattern combinations or their distributions, as well as anamnesis and clinical information. Since texture recognition has a very high accuracy percentage if a complex network approach is used, this paper aims to implement such a technique customized for diffuse interstitial lung diseases (DILD). The proposed procedure translates HRCT lung imaging into complex networks by taking samples containing a secondary lobule, converting them into complex networks and analyzing them in three dimensions: emphysema, ground glass opacity, and consolidation. This method was evaluated on a 60-patient lot and the results showed a clear, quantifiable difference between healthy and affected lungs. By deconstructing the image on three pathological axes, the method offers an objective way to quantify DILD details which, so far, have only been analyzed subjectively.

The aim of this study is to evaluate the test results of neonates delivered by COVID-19-positive mothers during pregnancy with those of neonates born to unvaccinated mothers who are COVID-19-free. A cohort study was conducted on 367 pregnant women who gave birth at Premiere Hospital, Timisoara, Romania, between May 2021 and February 2022. Two groups were established: Group 1, with 167 pregnant women infected with COVID-19, and Group 2, with 200 pregnant women who were not affected by COVID-19 during pregnancy. Maternal laboratory examination did not exhibit significant variations except for platelet count. In neonatal blood tests, WBC had a significantly lower median value in the group born to COVID-19-free mothers. Neonatal anemia and leukocytosis showed slightly higher prevalence in Group 1, but the differences were not statistically significant. This study suggests that maternal COVID-19 infection during pregnancy does not have significant associations with most maternal and neonatal characteristics.

Background/Objectives: Lung cancer remains a major cause of cancer-related mortality, with regional differences in incidence and patient characteristics. This study aimed to verify and quantify a perceived dramatic increase in lung cancer cases at a Romanian center, identify distinct patient phenotypes using unsupervised machine learning, and characterize contributing factors, including demographic shifts, changes in the healthcare system, and geographic patterns. Methods: A comprehensive retrospective analysis of 4206 lung cancer patients admitted between 2013 and 2024 was conducted, with detailed molecular characterization of 398 patients from 2023 to 2024. Temporal trends were analyzed using statistical methods, while k-means clustering on 761 clinical features identified patient phenotypes. The geographic distribution, smoking patterns, respiratory comorbidities, and demographic factors were systematically characterized across the identified clusters. Results: We confirmed an 80.5% increase in lung cancer admissions between pre-pandemic (2013–2020) and post-pandemic (2022–2024) periods, exceeding the 51.1% increase in total hospital admissions and aligning with national Romanian trends. Five distinct patient clusters emerged: elderly never-smokers (28.9%) with the highest metastatic rates (44.3%), heavy-smoking males (27.4%), active smokers with comprehensive molecular testing (31.7%), young mixed-gender cohort (7.3%) with balanced demographics, and extreme heavy smokers (4.8%) concentrated in rural areas (52.6%) with severe comorbidity burden. Clusters demonstrated significant differences in age (p < 0.001), smoking intensity (p < 0.001), geographic distribution (p < 0.001), as well as molecular characteristics. COPD prevalence was exceptionally high (44.8–78.9%) across clusters, while COVID-19 history remained low (3.4–8.3%), suggesting a limited direct association between the pandemic and cancer. Conclusions: This study presents the first comprehensive machine learning-based stratification of lung cancer patients in Romania, confirming genuine epidemiological increases beyond healthcare system artifacts. The identification of five clinically meaningful phenotypes—particularly rural extreme smokers and age-stratified never-smokers—demonstrates the value of unsupervised clustering for regional healthcare planning. These findings establish frameworks for targeted screening programs, personalized treatment approaches, and resource allocation strategies tailored to specific high-risk populations while highlighting the potential of artificial intelligence in identifying actionable clinical patterns for the implementation of precision medicine.