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Cureus May 2024Non-small cell lung carcinoma (NSCLC) is a prevalent and aggressive form of lung cancer, with a poor prognosis for metastatic disease. Immunotherapy, particularly immune... (Review)
Review
Non-small cell lung carcinoma (NSCLC) is a prevalent and aggressive form of lung cancer, with a poor prognosis for metastatic disease. Immunotherapy, particularly immune checkpoint inhibitors (ICIs), has revolutionized the management of NSCLC, but response rates are highly variable. Identifying reliable predictive biomarkers is crucial to optimize patient selection and treatment outcomes. This systematic review aimed to evaluate the current state of artificial intelligence (AI) and machine learning (ML) applications in predicting the response to immunotherapy in NSCLC. A comprehensive literature search identified 19 studies that met the inclusion criteria. The studies employed diverse AI/ML techniques, including deep learning, artificial neural networks, support vector machines, and gradient boosting methods, applied to various data modalities such as medical imaging, genomic data, clinical variables, and immunohistochemical markers. Several studies demonstrated the ability of AI/ML models to accurately predict immunotherapy response, progression-free survival, and overall survival in NSCLC patients. However, challenges remain in data availability, quality, and interpretability of these models. Efforts have been made to develop interpretable AI/ML techniques, but further research is needed to improve transparency and explainability. Additionally, translating AI/ML models from research settings to clinical practice poses challenges related to regulatory approval, data privacy, and integration into existing healthcare systems. Nonetheless, the successful implementation of AI/ML models could enable personalized treatment strategies, improve treatment outcomes, and reduce unnecessary toxicities and healthcare costs associated with ineffective treatments.
PubMed: 38939246
DOI: 10.7759/cureus.61220 -
The Pan African Medical Journal 2024Lymphatic filariasis is a neglected tropical disease that affects the lymphatic system of humans. The major etiologic agent is a nematode called Wuchereria bancrofti,... (Review)
Review Meta-Analysis
Lymphatic filariasis is a neglected tropical disease that affects the lymphatic system of humans. The major etiologic agent is a nematode called Wuchereria bancrofti, but Brugia malayi and Brugia timoriare sometimes encountered as causative agents. Mosquitoes are the vectors while humans the definitive hosts respectively. The burden of the disease is heavier in Nigeria than in other endemic countries in Africa. This occurs with increasing morbidity and mortality at different locations within the country, the World Health Organization recommended treatments for lymphatic filariasis include the use of Albendazole (400mg) twice per year in co-endemic areas with loa loa, Ivermectin (200mcg/kg) in combination with Albendazole (400mg) in areas that are co-endemic with onchocerciasis, ivermectin (200mcg/kg) with diethylcarbamazine citrate (DEC) (6mg/kg) and albendazole (400mg) in areas without onchocerciasis. This paper covered a systematic review, meta-analysis, and scoping review on lymphatic filariasis in the respective geopolitical zones within the country. The literature used was obtained through online search engines including PubMed and Google Scholar with the heading "lymphatic filariasis in the name of the state", Nigeria. This review revealed an overall prevalence of 11.18% with regional spread of Northwest (1.59%), North Central and North East, (4.52%), South West (1.26%), and South-South with South East (3.81%) prevalence. The disease has been successfully eliminated in Argungu local government areas (LGAs) of Kebbi State, Plateau, and Nasarawa States respectively. Most clinical manifestations (31.12%) include hydrocele, lymphedema, elephantiasis, hernia, and dermatitis. Night blood samples are appropriate for microfilaria investigation. Sustained MDAs, the right testing methods, early treatment of infected cases, and vector control are useful for the elimination of lymphatic filariasis for morbidity management and disability prevention in the country. Regional control strategies, improved quality monitoring of surveys and intervention programs with proper records of morbidity and disability requiring intervention are important approaches for the timely elimination of the disease in Nigeria.
Topics: Elephantiasis, Filarial; Humans; Nigeria; Animals; Wuchereria bancrofti; Filaricides; Albendazole; Neglected Diseases; Ivermectin; Brugia malayi
PubMed: 38933431
DOI: 10.11604/pamj.2024.47.142.39746 -
Iranian Journal of Public Health May 2024Infectious outbreaks due to disrupted social and environmental conditions after climate change-induced events complicate disasters. This research aimed to determine the... (Review)
Review
BACKGROUND
Infectious outbreaks due to disrupted social and environmental conditions after climate change-induced events complicate disasters. This research aimed to determine the contentions of bioclimatic variables and extreme events on the prevalence of the most common Climate-Sensitive Infectious Disease (CSID); Malaria in Iran.
METHODS
The present narrative systematic review study was conducted on the bioclimatic variable impact on the prevalence of malaria, as a common CSID. The search was conducted in 3 sections: global climate change-related studies, disaster related, and studies that were conducted in Iran. The literature search was focused on papers published in English and Persian from Mar 2000 to Dec 2021, using electronic databases; Scopus, Web of Science, PubMed, Google Scholar, SID, Magiran, and IranDoc.
RESULTS
Overall, 41 studies met the inclusion criteria. The various types of climatic variables including; Temperature, rainfall, relative humidity, and hydrological events including; flood, drought, and cyclones has been reported as a predictor of malaria. The results of studies, inappropriately and often were inconsistent in both Iran and other parts of the world.
CONCLUSION
Identifying malaria outbreak risks is essential to assess vulnerability, and a starting point to identify where the health system is required to reduce the vulnerability and exposure of the population. The finding of most related studies is not congruent to achieve reliable information, more extensive studies in all climates and regions of the country, by climatic models and high accuracy risk map, using the long period of bioclimatic variables and malaria trend is recommended.
PubMed: 38912133
DOI: 10.18502/ijph.v53i5.15584 -
Parasites & Vectors Jun 2024Severe fever with thrombocytopenia syndrome (SFTS) is an emerging tick-borne zoonosis caused by the SFTS virus (SFTSV). Understanding the prevalence of SFTSV RNA in... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Severe fever with thrombocytopenia syndrome (SFTS) is an emerging tick-borne zoonosis caused by the SFTS virus (SFTSV). Understanding the prevalence of SFTSV RNA in humans, vertebrate hosts and ticks is crucial for SFTS control.
METHODS
A systematic review and meta-analysis were conducted to determine the prevalence of SFTSV RNA in humans, vertebrate hosts and questing ticks. Nine electronic databases were searched for relevant publications, and data on SFTSV RNA prevalence were extracted. Pooled prevalence was estimated using a random effects model. Subgroup analysis and multivariable meta-regression were performed to investigate sources of heterogeneity.
RESULTS
The pooled prevalence of SFTSV RNA in humans was 5.59% (95% confidence interval [CI] 2.78-9.15%) in those in close contact (close contacts) with infected individuals (infected cases) and 0.05% (95% CI 0.00-0.65%) in healthy individuals in endemic areas. The SFTSV infection rates in artiodactyls (5.60%; 95% CI 2.95-8.96%) and carnivores (6.34%; 95% CI 3.27-10.23%) were higher than those in rodents (0.45%; 95% CI 0.00-1.50%). Other animals, such as rabbits, hedgehogs and birds, also played significant roles in SFTSV transmission. The genus Haemaphysalis was the primary transmission vector, with members of Ixodes, Dermacentor, and Amblyomma also identified as potential vectors. The highest pooled prevalence was observed in adult ticks (1.03%; 95% CI 0.35-1.96%), followed by nymphs (0.66%; 95% CI 0.11-1.50%) and larvae (0.01%; 95% CI 0.00-0.46%). The pooled prevalence in ticks collected from endemic areas (1.86%; 95% CI 0.86-3.14%) was higher than that in ticks collected in other regions (0.41%; 95% CI 0.12-0.81%).
CONCLUSIONS
Latent SFTSV infections are present in healthy individuals residing in endemic areas, and close contacts with SFTS cases are at a significantly higher risk of infection. The type of animal is linked to infection rates in vertebrate hosts, while infection rates in ticks are associated with the developmental stage. Further research is needed to investigate the impact of various environmental factors on SFTSV prevalence in vertebrate hosts and ticks.
Topics: Animals; Humans; Phlebovirus; Severe Fever with Thrombocytopenia Syndrome; Ticks; Vertebrates; Prevalence; RNA, Viral
PubMed: 38902842
DOI: 10.1186/s13071-024-06341-2 -
Acta Medica Philippina 2024The World Health Organization recently revised their recommendations and considered healthy children and adolescents as low priority group for COVID-19 vaccine. This...
OBJECTIVES
The World Health Organization recently revised their recommendations and considered healthy children and adolescents as low priority group for COVID-19 vaccine. This review comprehensively assessed existing clinical evidence on COVID-19 vaccine in 12-17 years old.
METHODS
Included in this review were any type of study that investigated the efficacy, immunogenicity, safety, and effectiveness of COVID-19 vaccine on protection against SARS-COV-2 infection in 12-17 years old. Various electronic databases were searched up to March 15, 2023. Studies were screened, data extracted, risk of bias appraised, and certainty of evidence was judged using GRADE. Review Manager 5.4 was used to estimate pooled effects. Difference between the two groups was described as mean difference for continuous variables and as relative risk or odds ratio for categorical variables.
RESULTS
There were six randomized controlled trials and 16 effectiveness studies (8 cohorts and 8 case control). Low certainty evidence showed that BNT162b2 (Pfizer) was effective, immunogenic, and safe in healthy adolescents. There were 15 effectiveness studies on BNT162b2 (Pfizer) in healthy adolescent and one on immunocompromised patients. It was protective against infection with any of the variants, with higher protection against Delta than Omicron. BNT162b2 is protective against hospitalization and emergency and urgent care (high certainty); and critical care and MIS-C (low). Very low certainty evidence noted that BNT 162b2 was also immunogenic in 12-21 years old with rheumatic diseases while on immunomodulatory treatment but with possible increased exacerbation of illness. Low certainty evidence demonstrated that mRNA-1273 (Moderna) was effective, immunogenic, and safe. Low to very low certainty evidence were noted on the safety and immunogenicity of two vector base vaccines (ChAdOx1-19 and Ad5 vector COVID vaccine) and two inactivated vaccines (CoronaVac and BBIBP CorV).
CONCLUSION
There is presently low certainty evidence on the use of RNA vaccines in 12-17 years old. The recommendation on its use is weak. There is presently insufficient evidence for the use of inactivated and vector-based COVID-19 vaccines. Different countries should consider whether to vaccinate healthy adolescent without comprising the other recommended immunization and health priorities that are crucial for this age group. Other factors including cost-effectiveness of vaccination and disease burden should be accounted.
PubMed: 38882914
DOI: 10.47895/amp.v58i7.7930 -
Frontiers in Aging Neuroscience 2024This meta-analysis aims to assess the effectiveness and safety of robot-assisted deep brain stimulation (DBS) surgery for Parkinson's disease(PD).
OBJECTIVE
This meta-analysis aims to assess the effectiveness and safety of robot-assisted deep brain stimulation (DBS) surgery for Parkinson's disease(PD).
METHODS
Four databases (Medline, Embase, Web of Science and CENTRAL) were searched from establishment of database to 23 March 2024, for articles studying robot-assisted DBS in patients diagnosed with PD. Meta-analyses of vector error, complication rate, levodopa-equivalent daily dose (LEDD), Unified Parkinson's Disease Rating Scale (UPDRS), UPDRS II, UPDRS III, and UPDRS IV were performed.
RESULTS
A total of 15 studies were included in this meta-analysis, comprising 732 patients with PD who received robot-assisted DBS. The pooled results revealed that the vector error was measured at 1.09 mm (95% CI: 0.87 to 1.30) in patients with Parkinson's disease who received robot-assisted DBS. The complication rate was 0.12 (95% CI, 0.03 to 0.24). The reduction in LEDD was 422.31 mg (95% CI: 68.69 to 775.94). The improvement in UPDRS, UPDRS III, and UPDRS IV was 27.36 (95% CI: 8.57 to 46.15), 14.09 (95% CI: 4.67 to 23.52), and 3.54 (95% CI: -2.35 to 9.43), respectively.
CONCLUSION
Robot-assisted DBS is a reliable and safe approach for treating PD. Robot-assisted DBS provides enhanced accuracy in contrast to conventional frame-based stereotactic techniques. Nevertheless, further investigation is necessary to validate the advantages of robot-assisted DBS in terms of enhancing motor function and decreasing the need for antiparkinsonian medications, in comparison to traditional frame-based stereotactic techniques.: PROSPERO(CRD42024529976).
PubMed: 38882524
DOI: 10.3389/fnagi.2024.1419152 -
Cureus May 2024Chronic kidney disease (CKD) is a progressive condition characterized by gradual loss of kidney function, necessitating timely monitoring and interventions. This... (Review)
Review
Chronic kidney disease (CKD) is a progressive condition characterized by gradual loss of kidney function, necessitating timely monitoring and interventions. This systematic review comprehensively evaluates the application of artificial intelligence (AI) and machine learning (ML) techniques for predicting CKD progression. A rigorous literature search identified 13 relevant studies employing diverse AI/ML algorithms, including logistic regression, support vector machines, random forests, neural networks, and deep learning approaches. These studies primarily aimed to predict CKD progression to end-stage renal disease (ESRD) or the need for renal replacement therapy, with some focusing on diabetic kidney disease progression, proteinuria, or estimated glomerular filtration rate (GFR) decline. The findings highlight the promising predictive performance of AI/ML models, with several achieving high accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve scores. Key factors contributing to enhanced prediction included incorporating longitudinal data, baseline characteristics, and specific biomarkers such as estimated GFR, proteinuria, serum albumin, and hemoglobin levels. Integration of these predictive models with electronic health records and clinical decision support systems offers opportunities for timely risk identification, early interventions, and personalized management strategies. While challenges related to data quality, bias, and ethical considerations exist, the reviewed studies underscore the potential of AI/ML techniques to facilitate early detection, risk stratification, and targeted interventions for CKD patients. Ongoing research, external validation, and careful implementation are crucial to leveraging these advanced analytical approaches in clinical practice, ultimately improving outcomes and reducing the burden of CKD.
PubMed: 38864072
DOI: 10.7759/cureus.60145 -
Cardiology in ReviewSeveral vaccines against coronavirus disease 2019 (COVID-19)-caused by the novel severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2)-have been developed since...
Several vaccines against coronavirus disease 2019 (COVID-19)-caused by the novel severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2)-have been developed since the COVID-19 pandemic began. Of these, 7 have been approved in the World Health Organization's Emergency Use Listing. However, these vaccines have been reported to have rare or serious adverse cardiovascular effects. This review presents updated information on the adverse cardiovascular effects of the approved COVID-19 vaccines-including inactivated vaccines, protein subunit vaccines, virus-like particles, nucleic acid vaccines, and viral vector vaccines-and the underlying mechanisms.
Topics: Humans; COVID-19 Vaccines; COVID-19; Cardiovascular Diseases; SARS-CoV-2
PubMed: 38848534
DOI: 10.1097/CRD.0000000000000508 -
Journal of Alzheimer's Disease : JAD Jun 2024Dementia is a general term for several progressive neurodegenerative disorders including Alzheimer's disease. Timely and accurate detection is crucial for early...
BACKGROUND
Dementia is a general term for several progressive neurodegenerative disorders including Alzheimer's disease. Timely and accurate detection is crucial for early intervention. Advancements in artificial intelligence present significant potential for using machine learning to aid in early detection.
OBJECTIVE
Summarize the state-of-the-art machine learning-based approaches for dementia prediction, focusing on non-invasive methods, as the burden on the patients is lower. Specifically, the analysis of gait and speech performance can offer insights into cognitive health through clinically cost-effective screening methods.
METHODS
A systematic literature review was conducted following the PRISMA protocol (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). The search was performed on three electronic databases (Scopus, Web of Science, and PubMed) to identify the relevant studies published between 2017 to 2022. A total of 40 papers were selected for review.
RESULTS
The most common machine learning methods employed were support vector machine followed by deep learning. Studies suggested the use of multimodal approaches as they can provide comprehensive and better prediction performance. Deep learning application in gait studies is still in the early stages as few studies have applied it. Moreover, including features of whole body movement contribute to better classification accuracy. Regarding speech studies, the combination of different parameters (acoustic, linguistic, cognitive testing) produced better results.
CONCLUSIONS
The review highlights the potential of machine learning, particularly non-invasive approaches, in the early prediction of dementia. The comparable prediction accuracies of manual and automatic speech analysis indicate an imminent fully automated approach for dementia detection.
PubMed: 38848181
DOI: 10.3233/JAD-231459 -
Psychology Research and Behavior... 2024Speech disorders profoundly impact the overall quality of life by impeding social operations and hindering effective communication. This study addresses the gap in... (Review)
Review
PURPOSE
Speech disorders profoundly impact the overall quality of life by impeding social operations and hindering effective communication. This study addresses the gap in systematic reviews concerning machine learning-based assistive technology for individuals with speech disorders. The overarching purpose is to offer a comprehensive overview of the field through a Systematic Literature Review (SLR) and provide valuable insights into the landscape of ML-based solutions and related studies.
METHODS
The research employs a systematic approach, utilizing a Systematic Literature Review (SLR) methodology. The study extensively examines the existing literature on machine learning-based assistive technology for speech disorders. Specific attention is given to ML techniques, characteristics of exploited datasets in the training phase, speaker languages, feature extraction techniques, and the features employed by ML algorithms.
ORIGINALITY
This study contributes to the existing literature by systematically exploring the machine learning landscape in assistive technology for speech disorders. The originality lies in the focused investigation of ML-speech recognition for impaired speech disorder users over ten years (2014-2023). The emphasis on systematic research questions related to ML techniques, dataset characteristics, languages, feature extraction techniques, and feature sets adds a unique and comprehensive perspective to the current discourse.
FINDINGS
The systematic literature review identifies significant trends and critical studies published between 2014 and 2023. In the analysis of the 65 papers from prestigious journals, support vector machines and neural networks (CNN, DNN) were the most utilized ML technique (20%, 16.92%), with the most studied disease being Dysarthria (35/65, 54% studies). Furthermore, an upsurge in using neural network-based architectures, mainly CNN and DNN, was observed after 2018. Almost half of the included studies were published between 2021 and 2022).
PubMed: 38835654
DOI: 10.2147/PRBM.S460283