The effect of machine learning algorithms in the prediction, and diagnosis of meningitis: A systematic review.

Background and Aims: This systematic review aimed to evaluating the effectiveness of machine learning (ML) algorithms for the prediction and diagnosis of meningitis. Methods: On November 12, 2022, a systematic review was carried out using a keyword search in the reliable scientific databases PubMed, EMBASE, Scopus, and Web of Science. The recommendations of Preferred Reporting for Systematic Reviews and Meta-Analyses (PRISMA) were adhered to. Studies conducted in English that employed ML to predict and identify meningitis were deemed to match the inclusion criteria. The eligibility requirements were used to independently review the titles and abstracts. The whole text was then obtained and independently reviewed in accordance with the eligibility requirements. Results: After all the research matched the inclusion criteria, a total of 16 studies were added to the systematic review. Studies on the application of ML algorithms in the three categories of disease diagnosis ability (8.16) and disease prediction ability (8.16) (including cases related to identifying patients (50%), risk of death in patients (25%), the consequences of the disease in childhood (12.5%), and its etiology [12.5%]) were placed. Among the ML algorithms used in this study, logistic regression (LR) (4.16, 25%) and multiple logistic regression (MLR) (4.16, 25%) were the most used. All the included studies indicated improvements in the processes of diagnosis, prediction, and disease outbreak with the help of ML algorithms. Conclusion: The results of the study showed that in all included studies, ML algorithms were an effective approach to facilitate diagnosis, predict consequences for risk classification, and improve resource utilization by predicting the volume of patients or services as well as discovering risk factors. The role of ML algorithms in improving disease diagnosis was more significant than disease prediction and prevalence. Meanwhile, the use of combined methods can optimize differential diagnoses and facilitate the decision-making process for healthcare providers.

Attitudes, knowledge, and skills towards artificial intelligence among healthcare students: A systematic review.

Background and Aims: This systematic review examined healthcare students' attitudes, knowledge, and skill in Artificial Intelligence (AI). Methods: On August 3, 2022, studies were retrieved from the PubMed, Embase, Scopus, and Web of Science databases. Preferred Reporting Items for Systematic Reviews and Meta-Analyses recommendations were followed. We included cross-sectional studies that examined healthcare students' knowledge, attitudes, skills, and perceptions of AI in this review. Using the eligibility requirements as a guide, titles and abstracts were screened. Complete texts were then retrieved and independently reviewed per the eligibility requirements. To collect data, a standardized form was used. Results: Of the 38 included studies, 29 (76%) of healthcare students had a positive and promising attitude towards AI in the clinical profession and its use in he future; however, in nine of the studies (24%), students considered AI a threat to healthcare fields and had a negative attitude towards it. Furthermore, 26 studies evaluated the knowledge of healthcare students about AI. Among these, 18 studies evaluated the level of student knowledge as low (50%). On the other hand, in six of the studies, students' high knowledge of AI was reported, and two of the studies reported average student general knowledge (almost 50%). Of the six studies, four (67%) of the students had very low skills, so they stated that they had never worked with AI. Conclusion: Evidence from this review shows that healthcare students had a positive and promising attitude towards AI in medicine; however, most students had low knowledge and limited skills in working with AI. Face-to-face instruction, training manuals, and detailed instructions are therefore crucial for implementing and comprehending how AI technology works and raising students' knowledge of the advantages of AI.

The effect of telerehabilitation on improving the physical activity of patients with osteoarthritis: A systematic review.

This systematic review aimed to evaluate the effect of telerehabilitation on improving physical activity, physical function, and quality of life in patients with osteoarthritis (OA). A systematic review of randomized controlled trial studies was conducted without a time limit by searching for keywords in the title, abstract, and study keywords in the scientific databases Embase, Web of Science, Scopus, and PubMed on October 20, 2021. This study was conducted according to Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. Titles and abstracts were retrieved based on the inclusion, exclusion, and quality assessment criteria. Then, full texts were retrieved and reviewed independently by two separate authors based on the eligibility criteria. Disputes were resolved through discussion. A form with the same predefined elements was used to extract data. Totally, eight eligible articles were found through this review. The studies on telerehabilitation approaches were categorized into three categories, which are: home-based exercise programs by online mobile applications, sports counseling and physiotherapist support via telephone calls, and Internet-based exercise training (IBET). In four studies (57%), telerehabilitation was effective in the short term for some months and improved the performance, self-efficacy, and quality of life of participants. On the other hand, in the long-term effects, there was no difference in participants' improvement (43%). In long-term follow-up, there was no difference between the efficiency of traditional rehabilitation and telerehabilitation in improving the physical performance and quality of life. However, telerehabilitation can be a viable alternative to traditional physiotherapy in patients with OA.

Prevalence of Listeria monocytogenes infection in women with spontaneous abortion, normal delivery, fertile and infertile.

BACKGROUND: Listeria monocytogenes with a vast range of natural reservoirs is more known for being a food-borne pathogen. Human infections have shown an impact on pregnancy outcomes, so, this study surveyed the frequency of L. monocytogenes infection involving different groups of women. METHODS: This study enrolled a total sample consisting of 109 women with spontaneous abortion, 109 women with normal delivery, 100 fertile women, and 99 infertile women aged 19-40 years and willing to participate in the study. The research tool in this study was a questionnaire and Polymerase chain reaction (PCR) test. RESULTS: According to the results, the frequency of L. monocytogenes infection was 4/109 (3.66%) observed among women with spontaneous abortion, 2/109 (1.83%) among women with normal delivery, 3/100 (3%) among fertile women, and 0/99 (0%) among infertile women. CONCLUSION: There was no significant relationship between Listeria monocytogenes infection and pregnancy outcomes of spontaneous abortion and infertility.

Factors Affecting the Product Life Cycle of Generic Medicines.

Background: Product life cycle (PLC) refers to the time ranging from when a product is introduced into the market to when it is taken off the shelves. The PLC management can guarantee product survival and prevent its decline. Objectives: This study investigated generic antibiotic PLCs and detected factors affecting them in the competitive pharmaceutical market of Iran to improve the PLC management of such drugs. Methods: To study the PLC of antibiotics, data were collected from 2002 to 2017, and then the PLC curves were analyzed. Accordingly, factors affecting the PLC of antibiotics were illustrated in two sections: all PLC curves and the PLC curves with one sales peak. Using a generalized linear model combined with a machine learning approach, we identified the sales patterns and the effect of the product-related and the competition-related factors on the PLC curves, peak height, and the time to reach peak sales. Results: According to the findings, 16, 11.87, 13.03, and 59% of the antibiotics had linear, binomial, one-peak, and oscillating sales patterns, respectively. The most crucial factors affecting the PLC shape were the quality, microbial spectrum, dosage forms, number of competitors, and entry arrangement. Conclusions: This study examined factors affecting the PLC patterns of generic pharmaceutical products. The findings would provide more insights into the generic pharmaceutical market as one of the less-studied markets in many countries.

Prevalence of Neisseria gonorrhoeae in Western Iran.

Neisseria gonorrhoeae is the causative agent of the sexually transmitted disease gonorrhoea. This bacterium infects the epithelial cells of the cervix of women and the urethra of men. However, its disease symptoms in the lower genitalia are found only in a small percentage of people. This study aimed to compare the frequency of N. gonorrhoeae genital infection among two groups of pregnant women, those with spontaneous abortions and those with normal pregnancies. This cross-sectional study was conducted in Western Iran. It included 417 women: 109 of whom had spontaneous abortions, 109 had normal deliveries, 100 were fertile, and 99 were infertile. Specific primers were used and DNA was extracted by endocervical swabs. A polymerase chain reaction test was then performed to detect N. gonorrhoeae. Data analysis was performed using the chi-squared test and t-tests. In all the above steps, a level of 5% was considered statistically significant, and the average ages in women with normal delivery, women with spontaneous abortion, fertile women, and infertile women were 27.8 ± 4.87, 29.6 ± 5.9, 32.1 ± 5.1, and 29.1 ± 6.3 years, respectively. The total frequency of N. gonorrhoeae infection was 0 (0%). The prevalence of N. gonorrhoeae infection was zero, and the disease was not associated with spontaneous abortion or infertility.

Risk Factors, Clinical Symptoms, Laboratory Findings and Imaging of Pregnant Women Infected with COVID-19 in North of Iran.

BACKGROUND: Due to the physiological changes in the body during pregnancy, the increased susceptibility to viral infections during this period and also the high prevalence of coronavirus disease 2019 (COVID-19) in the Guilan province, Iran, this study aimed to evaluate risk factors, clinical symptoms, laboratory findings and imaging of pregnant mothers with COVID-19. METHODS: In this descriptive study, 70 pregnant women aged 17-41 years with COVID-19 who were hospitalized from early March to late April 2020 were enrolled. Sampling was performed by census and from all hospitals in Guilan. The research instruments included a researcher-made questionnaire, including demographic characteristics, clinical symptoms, medical examinations, and paraclinical results. Data were analyzed with SPSS version 16. Frequency and percent were used to describe qualitative variables; for quantitative variables, if they were normally distributed, mean and standard deviation were used, and if they were non-normal, median and interquartile range (IQR) were used. RESULTS: The most severe symptoms recorded in mothers at the time of hospitalization were fever (47%), shortness of breath (16%) and cough (15%), respectively. One of 68 (1%) was in the severe stage of the disease and two mothers (2%) were in critical condition and admitted to the intensive care unit and finally died. Fifty-five of 66 women (83%) had lymphopenia, 22 of 42 (52%) tested positive on PCR, and 30 of 33 (90%) had an increase in lactate dehydrogenase (LDH) levels.Results showed that 15 of 32 patients who gave birth had preterm delivery (46%). CONCLUSION: The most common manifestations of the disease in pregnant women were fever, cough and shortness of breath, and in some cases muscle pain. The most common laboratory finding in infected mothers was lymphopenia. Complications of pregnancy and childbirth in women included an increase in cesarean delivery.

A Conformational Analysis Study on the Melanocortin 4 Receptor Using Multiple Molecular Dynamics Simulations.

Taking into account the uncertainties involved in 3D model of biomolecule developed by homology modeling (HM), it is important to opportunely validate the initial structure before employing for different purposes such as drug design. Extended simulation times and the necessity of correct representation of interactions within the protein and the nearby molecules impose significant limitations on molecular dynamics (MD)-based refinement of structures developed by HM. Consequently, there is a pressing requirement for more efficient methods for HM and subsequent validation of developed structure. Multiple MD simulation runs are well suited for producing ensembles of structures. In this context, a computational investigation was presented to study the structure of melanocortin 4 receptor (MC4R) using molecular dynamics (MD) simulations in explicit phospholipids bilayer. Several MD runs with different initial velocities were employed to sample conformations in the neighborhood of the native structure of receptor, collecting trajectories spanning 0.21 ms. The coherence between the results, different structural analysis, and the convergence of parameters derived by principal component analysis (PCA) shows that an accurate description of the MC4R conformational space around the native state was achieved by multiple MD trajectories.

Detection of genital mycoplasmal infections among infertile females by multiplex PCR.

BACKGROUND AND OBJECTIVES: Women reproductive system is a suitable environment for growth of various pathogen and nonpathogenic microorganisms. Mycoplasmataceae is a family of bacteria which cause oligosymptomatic genital infections. The complications caused by these bacteria may lead to infertility in women. The aim of this study was detection of genital Mycoplasma hominis, Ureaplasmaurealyticum and Mycoplasma genitalium among infertile females who referred to the infertility clinics. MATERIALS AND METHODS: A total of 104 infertile women (in reproductive age) who referred to infertility clinics in the city of Sanandaj, Kurdistan, Iran, from February to May 2013 were selected for this study. Cervical swabs were collected from all patients. The presence of genital Mycoplasmas was detected by multiplex-PCR. All data were analyzed statistically. RESULTS: Out of 104 patients, 39 cases (37.5%) were infected with U. urealyticum. Mycuplasma. genitalium and M. homimis were detected in 3 (2.9%) of the infertile women. Co-infection was seen in 3.8% of the patients. There was no statistically the infections and patient age, educational levels, literacy, situation of employment, age of first significant sexual intercourse, history of abortion, type of infertility and infertility duration (p value > 0.05). CONCLUSIONS: The data showed a low percentage of infection for M. genitalium and M. hominis in the studied women while the prevalence of U. urealyticum was high. Despite having no symptoms of an ongoing acute inflammation of the reproductive tract, many women may have genital mycoplasmas in the cervix. We concluded that multiplex PCR using a pair of primers is a useful and cost-effective method for diagnosis of female genital infections.

Mycoplasma genitalium and cancer: a brief review.

Approximately, 15-20% of all cancers worldwide are caused by infectious agents. Understanding the role of infectious agents on cancer development might be useful for developing new approaches to its prevention. Mycoplasma genitalium is a clinically important sexually transmitted pathogen that has been associated with several human diseases. There have been a few studies suggestive of probable roles of Mycoplasma genitalium in cancer development, including prostate and ovarian cancers and lymphomas, but the role of this microorganism like other Mycoplasma species in neoplasia is still conjectural. Considering the prevalence of Mycoplasma genitalium infections and also the emergence of resistant strains, Mycoplasma genitalium needs more attention in the infectious agent cancer-causing research area.