Opportunities and challenges of artificial intelligence and distributed systems to improve the quality of healthcare service.

The healthcare sector, characterized by vast datasets and many diseases, is pivotal in shaping community health and overall quality of life. Traditional healthcare methods, often characterized by limitations in disease prevention, predominantly react to illnesses after their onset rather than proactively averting them. The advent of Artificial Intelligence (AI) has ushered in a wave of transformative applications designed to enhance healthcare services, with Machine Learning (ML) as a noteworthy subset of AI. ML empowers computers to analyze extensive datasets, while Deep Learning (DL), a specific ML methodology, excels at extracting meaningful patterns from these data troves. Despite notable technological advancements in recent years, the full potential of these applications within medical contexts remains largely untapped, primarily due to the medical community's cautious stance toward novel technologies. The motivation of this paper lies in recognizing the pivotal role of the healthcare sector in community well-being and the necessity for a shift toward proactive healthcare approaches. To our knowledge, there is a notable absence of a comprehensive published review that delves into ML, DL and distributed systems, all aimed at elevating the Quality of Service (QoS) in healthcare. This study seeks to bridge this gap by presenting a systematic and organized review of prevailing ML, DL, and distributed system algorithms as applied in healthcare settings. Within our work, we outline key challenges that both current and future developers may encounter, with a particular focus on aspects such as approach, data utilization, strategy, and development processes. Our study findings reveal that the Internet of Things (IoT) stands out as the most frequently utilized platform (44.3 %), with disease diagnosis emerging as the predominant healthcare application (47.8 %). Notably, discussions center significantly on the prevention and identification of cardiovascular diseases (29.2 %). The studies under examination employ a diverse range of ML and DL methods, along with distributed systems, with Convolutional Neural Networks (CNNs) being the most commonly used (16.7 %), followed by Long Short-Term Memory (LSTM) networks (14.6 %) and shallow learning networks (12.5 %). In evaluating QoS, the predominant emphasis revolves around the accuracy parameter (80 %). This study highlights how ML, DL, and distributed systems reshape healthcare. It contributes to advancing healthcare quality, bridging the gap between technology and medical adoption, and benefiting practitioners and patients.

Hypoxemic Respiratory Failure and Coccidioidomycosis-Associated Acute Respiratory Distress Syndrome.

Background: Severe coccidioidomycosis presenting with respiratory failure is an uncommon manifestation of disease. Current knowledge of this condition is limited to case reports and small case series. Methods: A retrospective multicenter review of patients with coccidioidomycosis-associated acute respiratory distress syndrome (CA-ARDS) was conducted. It assessed clinical and laboratory variables at the time of presentation, reviewed the treatment course, and compared this cohort with a national database of patients with noncoccidioidomycosis ARDS. Survivors and nonsurvivors of coccidioidomycosis were also compared to determine prognostic factors. Results: In this study, CA-ARDS (n = 54) was most common in males, those of Hispanic ethnicity, and those with concurrent diabetes mellitus. As compared with the PETAL network database (Prevention and Early Treatment of Acute Lung Injury; n = 1006), patients with coccidioidomycosis were younger, had fewer comorbid conditions, and were less acidemic. The 90-day mortality was 15.4% for patients with coccidioidomycosis, as opposed to 42.6% (P < .0001) for patients with noncoccidioidomycosis ARDS. Patients with coccidioidomycosis who died, as compared with those who survived, were older, had higher APACHE II scores (Acute Physiology and Chronic Health Evaluation), and did not receive corticosteroid therapy. Conclusions: CA-ARDS is an uncommon but morbid manifestation of infection. When compared with a national database, the overall mortality appears favorable vs other causes of ARDS. Patients with CA-ARDS had a low overall mortality but required prolonged antifungal therapy. The utility of corticosteroids in this condition remains unconfirmed.

Dietary inflammatory index and neuropsychiatric disorders.

Neuropsychiatric disorders (NPDs) are considered a potential threat to mental health. Inflammation predominantly plays a role in the pathophysiology of NPDs. Dietary patterns are widely postulated to be involved in the physiological response to inflammation. This review aims to discuss the literature on how dietary inflammatory index (DII) is related to inflammation and, consequently, NPDs. After comprehensive scrutiny in different databases, the articles that investigated the relation of DII score and various NPDs and psychological circumstances were included. The association between dietary patterns and mental disorders comprising depression, anxiety, and stress proved the role of a proinflammatory diet in these conditions' exacerbation. Aging is another condition closely associated with DII. The impact of proinflammatory and anti-inflammatory diet on sleep quality indicated related disorders like sleep latency and day dysfunctions among the different populations are in relation with the high DII score. The potential effects of genetic backgrounds, dietary patterns, and the gut microbiome on DII are discussed as well. To plan preventive or therapeutic interventions considering the DII, these factors, especially genetic variations, should be considered as there is a growing body of literature indicating the role of personalized medicine in different NPDs. To the best of our knowledge, there is a limited number of RCTs on this subject, so future research should evaluate the causality via RCTs and look for therapeutic interventions with an eye on personalized medicine using information about DII in NPDs.

A Clinicopathological Categorization System for Clinical Research in Coccidioidomycosis.

A wide array of clinical manifestations follow infection with Coccidioides immitis or Coccidioides posadasii, ranging from asymptomatic infection to life-threatening pulmonary disease or extrapulmonary dissemination and meningitis. Epidemiological studies require consistent definitions of cases and their comparative clinical features. Understanding host and pathogen determinants of the severity of coccidioidomycosis also requires that specific clinical features (such as coccidioidal meningitis) and their overlap be precisely defined and quantified. Here we propose a system for categorization of outcomes of coccidioidomycosis in individuals who are not overtly immunocompromised that harmonizes clinical assessments during translational research of this increasingly common disease.

Spurious Presentation of Pulmonary Kaposi Sarcoma as Unresolved Pneumonia Led to Fatal Outcome.

Acquired immunodeficiency syndrome (AIDS)-associated Kaposi sarcoma (KS) is an angioproliferative neoplasia caused by infection with human herpesvirus 8 (HHV-8). It typically presents with mucocutaneous involvement, but it can be disseminated. Initial presentation with primarily pulmonary KS is rare. We present a case of a 32-year-old male with untreated human immunodeficiency virus (HIV) diagnosed 1 year before presentation who developed progressively worsening cough and shortness of breath for 6 months. He was hospitalized twice and treated for unresolved pneumonia in an outside hospital. The patient concomitantly developed purplish nodules on his face, then the upper trunk, back, chest, and thighs bilaterally that gradually increased in size and number. Histopathology findings from skin lesions were consistent for KS. Bronchoscopy found multiple erythematous plaques throughout the tracheobronchial tree with telangiectasias and inflammation suggestive of pulmonary KS. His imaging findings and positive serum HHV-8 polymerase chain reaction (PCR) were consistent with disseminated KS. He started antiretroviral therapy (ART) to treat his HIV infection, followed by liposomal doxorubicin chemotherapy. But both ART and chemotherapy were interrupted due to adherence and insurance issues. The patient was readmitted with acute respiratory failure requiring mechanical ventilation with multiple vasopressors that led to the patient's demise. The late recognition of KS diagnosis and delayed treatment can lead to worse outcomes.

A Unique Case of COVID-19 Presenting as Focal Seizures With Impaired Awareness.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has very rapidly become a global pandemic with millions of confirmed cases worldwide. In early 2021, viral encephalitis was the first neurological complication associated with COVID-19 and since then rise in cases has been reported with this association. A review highlighting 3 potential mechanisms linking a correlation between seizures and COVID-19 was previously reported. Herein described is a unique case of SARS-CoV2 infection that presented with focal seizure with impaired awareness.

The applications of machine learning techniques in medical data processing based on distributed computing and the Internet of Things.

Medical data processing has grown into a prominent topic in the latest decades with the primary goal of maintaining patient data via new information technologies, including the Internet of Things (IoT) and sensor technologies, which generate patient indexes in hospital data networks. Innovations like distributed computing, Machine Learning (ML), blockchain, chatbots, wearables, and pattern recognition can adequately enable the collection and processing of medical data for decision-making in the healthcare era. Particularly, to assist experts in the disease diagnostic process, distributed computing is beneficial by digesting huge volumes of data swiftly and producing personalized smart suggestions. On the other side, the current globe is confronting an outbreak of COVID-19, so an early diagnosis technique is crucial to lowering the fatality rate. ML systems are beneficial in aiding radiologists in examining the incredible amount of medical images. Nevertheless, they demand a huge quantity of training data that must be unified for processing. Hence, developing Deep Learning (DL) confronts multiple issues, such as conventional data collection, quality assurance, knowledge exchange, privacy preservation, administrative laws, and ethical considerations. In this research, we intend to convey an inclusive analysis of the most recent studies in distributed computing platform applications based on five categorized platforms, including cloud computing, edge, fog, IoT, and hybrid platforms. So, we evaluated 27 articles regarding the usage of the proposed framework, deployed methods, and applications, noting the advantages, drawbacks, and the applied dataset and screening the security mechanism and the presence of the Transfer Learning (TL) method. As a result, it was proved that most recent research (about 43%) used the IoT platform as the environment for the proposed architecture, and most of the studies (about 46%) were done in 2021. In addition, the most popular utilized DL algorithm was the Convolutional Neural Network (CNN), with a percentage of 19.4%. Hence, despite how technology changes, delivering appropriate therapy for patients is the primary aim of healthcare-associated departments. Therefore, further studies are recommended to develop more functional architectures based on DL and distributed environments and better evaluate the present healthcare data analysis models.

What You See Is Not What You Get: Germ Cell Tumor Mimicking Disseminated Coccidioidomycosis.

We present the case of a 30-year-old man with no prior medical history who presented to the hospital with a myriad of symptoms such as shortness of breath, generalized weakness, lower limb weakness, and urinary retention. He was recently diagnosed with "disseminated coccidioidomycosis" by an outside provider on an outpatient basis and started on fluconazole orally. However, due to a lack of improvement and significant symptoms, he was sent to the hospital to initiate liposomal amphotericin B treatment. After a comprehensive workup, an alternative diagnosis was suspected and eventually confirmed as metastatic germ cell carcinoma. Due to the vast dissemination and his poor functional status despite chemotherapy initiation, the patient elected for palliative care and expired shortly after at hospice. This case demonstrates the similarity of clinical findings between disseminated infections and malignancies.

From neurodevelopmental to neurodegenerative disorders: Investigating symptoms of attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) in patients with dementia.

Dementia is characterized by a progressive cognitive decline that could be caused by several disorders. Autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) are two prevalent neurodevelopmental disorders that might overlap with dementia symptoms. Hence, this study aimed to evaluate the ASD and ADHD symptoms in dementia patients referred to a memory clinic in Iran. We recruited 65 dementia patients and instructed them to fill out the autism quotient (AQ) and the Conners' Adult ADHD Rating Scales (CAARS) questionnaires. Considering the cutoff points of AQ and CAARS questionnaires, 18.5% of participants were at higher risk of ASD, and 35.4% were at higher risk of ADHD. The results indicated that ADHD and ASD symptoms might be common comorbidities in patients with dementia which can increase the disease burden. Specialized ADHD and ASD screening tools in the elderly population with dementia are needed to prevent misdiagnoses due to symptom overlaps.

The Potential Role of Immunotherapy in Wilms' Tumor: Opportunities and Challenges.

Wilms' tumor (WT) is the most common renal malignancy in children, accounting for more than 90% of all pediatric renal cancers. Although this tumor is generally responsive to treatment, relapses and deaths still occur in a significant proportion of patients. The genetic alterations commonly found in WT and also its unique histological features and the tumor microenvironment suggest that the immune system may play a crucial role in the disease's development and progression. The limitations of conventional therapies, including surgery, chemotherapy, and radiotherapy, in preventing recurrence in WT patients and their potential for exerting long-term side effects, necessitate the application of novel therapeutic strategies, like immunotherapy, in this disease. Immunotherapy is an emerging cancer treatment approach based on the concept of harnessing the patient's immune system to fight tumor cells. This approach has demonstrated promising results in various types of cancers due to its relatively high specificity, efficacy, and tolerability. However, the precise effects of immunotherapy in WT remain to be explored. For this purpose, this review highlights the potential implication of different immunotherapy approaches, like monoclonal antibodies, adoptive cell therapy, and immune checkpoint inhibitors, in patients with WT, with a particular emphasis on the tumor's genetic and histological features. Although much remains to be learned about the optimal use of immunotherapy for this disease, the available evidence suggests that immunotherapy has the potential to significantly improve outcomes for patients with WT. However, there is still a substantial need for conducting further studies, especially randomized controlled trials, to determine the most effective immunotherapy strategy for this tumor. Moreover, the potential beneficiary roles of the combination of immunotherapy and conventional treatments should be investigated in future research.

The role of neurofilament light chain in COVID­19: A potential prognostic biomarker.

Coronavirus disease 2019 (COVID­19) is an unprecedented global health concern that was declared a pandemic in March 2020. Although primarily recognized by respiratory symptoms, growing evidence suggested the causal relationship between the infection with the disease agent, namely severe acute respiratory coronavirus 2 (SARS­CoV­2), and neurological manifestations. Given that the virus­induced neurological involvement is associated with a poorer prognosis, persistent neurological sequelae, and a more severe form of the disease, efforts have been made to introduce a biomarker to recognize neurological abnormalities early in the course of the disease. Studies indicate a significantly higher concentration of neurofilament light chain (NFL) in cerebrospinal fluid or blood of COVID­19 patients versus adjusted controls. It has also been reported that COVID­19 patients suffering from the severe form of the disease had higher NFL levels than patients with mild to moderate forms. Moreover, elevated NFL levels at hospital admission in patients who did not present primarily with neurological expressions could predict the emergence of neurological symptoms during the hospital stay. The early recognition of neurological abnormalities using the NFL biomarker could lead to escalated medical care limiting the progression of SARS­CoV­2­induced central nervous system pathogenesis, resulting in a significant amelioration in disease outcome. Nevertheless, NFL assessment integrated with the evaluation of other neurodegenerative biomarkers and factors indicating disease prognosis could provide a more comprehensive estimate of disease prognosis and the extent of neurological involvement.

A new lung cancer detection method based on the chest CT images using Federated Learning and blockchain systems.

With an estimated five million fatal cases each year, lung cancer is one of the significant causes of death worldwide. Lung diseases can be diagnosed with a Computed Tomography (CT) scan. The scarcity and trustworthiness of human eyes is the fundamental issue in diagnosing lung cancer patients. The main goal of this study is to detect malignant lung nodules in a CT scan of the lungs and categorize lung cancer according to severity. In this work, cutting-edge Deep Learning (DL) algorithms were used to detect the location of cancerous nodules. Also, the real-life issue is sharing data with hospitals around the world while bearing in mind the organizations' privacy issues. Besides, the main problems for training a global DL model are creating a collaborative model and maintaining privacy. This study presented an approach that takes a modest amount of data from multiple hospitals and uses blockchain-based Federated Learning (FL) to train a global DL model. The data were authenticated using blockchain technology, and FL trained the model internationally while maintaining the organization's anonymity. First, we presented a data normalization approach that addresses the variability of data obtained from various institutions using various CT scanners. Furthermore, using a CapsNets method, we classified lung cancer patients in local mode. Finally, we devised a way to train a global model cooperatively utilizing blockchain technology and FL while maintaining anonymity. We also gathered data from real-life lung cancer patients for testing purposes. The suggested method was trained and tested on the Cancer Imaging Archive (CIA) dataset, Kaggle Data Science Bowl (KDSB), LUNA 16, and the local dataset. Finally, we performed extensive experiments with Python and its well-known libraries, such as Scikit-Learn and TensorFlow, to evaluate the suggested method. The findings showed that the method effectively detects lung cancer patients. The technique delivered 99.69 % accuracy with the smallest possible categorization error.

A Unique Case of Pseudomonas aeruginosa-Associated Diarrhea in a Long-Term Hospitalized Adult Patient.

A 53-year-old Caucasian man with a history of alcohol use disorder, hypertension, and hypothyroidism presented with a myxedema coma requiring intubation. He had a complicated hospital course with ventilator-associated pneumonia with MRSA, sepsis with candida, and abdominal compartment syndrome requiring decompressive laparotomy. The patient slowly recovered during 43 days of hospitalization. During the intensive care unit (ICU) stay, a flexi-seal rectal tube was placed due to fecal incontinence. After being moved to a regular medicine unit, he started having loose watery stools with leukocytosis and neutrophilia. Clostridium difficile (C. diff.) colitis was suspected, and he was placed on oral vancomycin empirically. His stool test for C. diff. came back negative, and his rectal tube was subsequently removed. Imaging did not show any abscess, perforated viscus, or fistula formations. His stool culture grew a heavy colony of Pseudomonas aeruginosa (P. aeruginosa). Vancomycin was stopped, and he was started on oral ciprofloxacin 750 mg twice a day with complete resolution of his diarrhea and leukocytosis.

Multicentric Case Series and Literature Review of Coccidioidal Otomastoiditis.

Coccidioidomycosis involving the ear, mastoid bone, or both is uncommon. We describe 5 new cases from the United States and review 4 cases reported in the literature of otomycosis and mastoiditis caused by Coccidioides. Of the 9 cases, 8 were linked to residence in or travel to California. Two patients had poorly controlled diabetes mellitus, 7 had otomastoiditis, 1 had otitis externa without mastoid involvement, and 1 had mastoiditis without otic involvement. Four patients had concurrent or prior pulmonary coccidioidomycosis. Ipsilateral facial nerve palsies developed in 2 patients. All patients received antifungal treatment for varying durations, and 8 of the 9 patients underwent surgical debridement. Clinicians should consider coccidioidomycosis as a differential diagnosis for otomastoiditis in patients with geographic risks.

Coccidioidal Pulmonary Cavitation: A New Age.

Coccidioides species are thermally dimorphic fungi found in geographically defined areas of the Western Hemisphere. The primary portal of entry is respiratory, with symptomatic pneumonic diseases as the most common presentation. Subsequent pulmonary complications as well as extrapulmonary metastatic infection may occur, either of which may be the presenting disease manifestation. Cavitary lung disease may be found incidentally or when investigating symptoms such as cough or hemoptysis. This study aims to explore the spectrum of coccidioidal cavities and the evaluation and management in a cohort of patients seen at Kern Medical over the last 12 years.

Asymptomatic Coccidioidal Meningitis Relapse: A Demon in Disguise.

Coccidioides spp is a soil-dwelling, dimorphic fungus that causes coccidioidomycosis. It is endemic to the western hemisphere. Although primarily a respiratory disease, it can also cause a myriad of clinical manifestations, from asymptomatic disease to meningitis. In fact, Coccidioides species is probably the most common etiologic agent of long-term meningitis in California and Arizona. Early diagnosis and treatment are critical to avoid fatal complications. With treatment, the cerebral spinal fluid analysis may return to normal. Relapse of coccidioidal meningitis is usually suspected with recurrence of meningitis symptoms. The patient is a 53-year-old man with a 2-decade history of coccidioidal meningitis who was diagnosed with an asymptomatic relapse of coccidioidal meningitis.

Primary Mpox and Secondary Syphilis in an HIV Patient: A Community Hospital Experience.

Mpox was an emerging outbreak in California in 2022, primarily in major metropolitan areas, as part of the global mpox outbreak declared by World Health Organization in July 2022. Community hospitals outside of major metropolitan areas have seen fewer cases to date, so they may be less equipped to diagnose and treat patients with mpox. They may have limited public health resources commensurate with the area's population density. Mpox may also be superimposed on ongoing local outbreaks of other sexually transmitted infections. We present a case of a person with HIV who contracted mpox and also developed secondary syphilis. Early detection can be beneficial for prompt treatment, decreased burden of disease for the individual, and prevention of further spread of the infection.

The Association Between Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder Symptoms in Medical Students.

ABSTRACT: Autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) are the two most prevalent neurodevelopmental disorders affecting communication and behavior. The co-occurrence of these conditions is probable and can contribute to several challenges in adaptive functioning and academic achievement.In this cross-sectional study, 168 Iranian medical students (107 female, 61 male) studying at Tehran University of Medical Sciences in 2021 were enrolled. We administered the Ritvo Autism Asperger Diagnostic Scale-Revised (RAADS-R) and Adult ADHD Self-Report Scale (ASRS) questionnaires online to assess different symptoms of ASD and ADHD in our sample. In this study, the RAADS-R was translated into Persian and validated for the first time in Iran.The correlation tests demonstrated a significant association between the total score and different subscales' scores of the RAADS-R and the total score and the two subscales' scores of the ASRS questionnaire ( p < 0.001, 0.27 < Spearman correlation coefficient < 0.51). This study also illustrated a high prevalence of ASD and ADHD symptoms among the participants. Moreover, male respondents had a significantly higher prevalence of ASD symptoms (57.3% in males vs . 28.03% in females, p < 0.001).This study indicated that the distinct impairments in behavior and cognition attributed to ASD and ADHD could be common manifestations in medical students. Given that the co-occurrence of these disorders may lead to significant challenges in their professional life, the early diagnosis and subsequent support for medical students with co-occurring expressions of ASD and ADHD could be extremely helpful, as it could indirectly improve the medical services provided to patients by future physicians, leading to an improvement in public health.

Isavuconazole in the Treatment of Chronic Forms of Coccidioidomycosis.

Coccidioidomycosis is a fungal infection with a range of clinical manifestations. Currently used antifungal agents exhibit variable efficacy and toxicity profiles that necessitate evaluation of additional therapeutic options. Improvement was observed in the majority of patients treated with isavuconazole, with clinical failures observed only in those with coccidioidal meningitis.

A Case of Coccidioidal Meningitis With Biofilm Obstructing VP Shunt Due to Cutibacterium acnes.

Herein described is a case of biofilm obstructing ventriculoperitoneal shunt due to Cutibacteirum acnes infection in a patient with coccidioidal meningitis. Cutibacterium acnes infects and obstructs cerebral shunts by the production of biofilm; however, diagnosis is usually missed by routine aerobic cultures. Obtaining anaerobic cultures routinely in patients with foreign body implants leading to central nervous system infections could prevent a missed diagnosis of this pathogen. Penicillin G is the first-line treatment.