Population-Based Artificial Intelligence Assessment of Relationship Between the Risk Factors for Diabetic Retinopathy in Indian Population.

PURPOSE: Risk factors (RFs), like 'body mass index (BMI),' 'age,' and 'gender' correlate with Diabetic Retinopathy (DR) diagnosis and have been widely studied. This study examines how these three secondary RFs independently affect the predictive capacity of primary RFs. METHODS: The dataset consisted of four population-based studies on the prevalence of DR and associated RFs in India between 2001 and 2010. An Autoencoder was employed to categorize RFs as primary or secondary. This study evaluated six primary RFs coupled independently with each secondary RF on five machine-learning models. RESULTS: The secondary RF 'gender' gave a maximum increase in Area under the curve (AUC) score to predict DR when combined separately with 'insulin treatment,' 'fasting plasma glucose,' 'hypertension history,' and 'glycosylated hemoglobin' with a maximum increase in AUC for the Naive Bayes model from 0.573 to 0.646, for the Support Vector Machines (SVM) model from 0.644 to 0.691, for the SVM model from 0.487 to 0.607, and for the Decision Tree model from 0.8 to 0.848, respectively. The secondary RFs 'age' and 'BMI' gave a maximum increase in AUC score to predict DR when combined separately with 'diabetes mellitus duration' and 'systolic blood pressure,' with a maximum increase in AUC for the SVM model from 0.389 to 0.621, and for the Decision Tree model from 0.617 to 0.713, respectively. CONCLUSION: The risk factor 'gender' was the best secondary RF in predicting DR compared to 'age' and 'BMI,' increasing the predictive power of four primary RFs.

Machine Learning-Based Diagnosis and Ranking of Risk Factors for Diabetic Retinopathy in Population-Based Studies from South India.

This paper discusses the importance of investigating DR using machine learning and a computational method to rank DR risk factors by importance using different machine learning models. The dataset was collected from four large population-based studies conducted in India between 2001 and 2010 on the prevalence of DR and its risk factors. We deployed different machine learning models on the dataset to rank the importance of the variables (risk factors). The study uses a t-test and Shapely additive explanations (SHAP) to rank the risk factors. Then, it uses five machine learning models (K-Nearest Neighbor, Decision Tree, Support Vector Machines, Logistic Regression, and Naive Bayes) to identify the unimportant risk factors based on the area under the curve criterion to predict DR. To determine the overall significance of risk variables, a weighted average of each classifier's importance is used. The ranking of risk variables is provided to machine learning models. To construct a model for DR prediction, the combination of risk factors with the highest AUC is chosen. The results show that the risk factors glycosylated hemoglobin and systolic blood pressure were present in the top three risk factors for DR in all five machine learning models when the t-test was used for ranking. Furthermore, the risk factors, namely, systolic blood pressure and history of hypertension, were present in the top five risk factors for DR in all the machine learning models when SHAP was used for ranking. Finally, when an ensemble of the five machine learning models was employed, independently with both the t-test and SHAP, systolic blood pressure and diabetes mellitus duration were present in the top four risk factors for diabetic retinopathy. Decision Tree and K-Nearest Neighbor resulted in the highest AUCs of 0.79 (t-test) and 0.77 (SHAP). Moreover, K-Nearest Neighbor predicted DR with 82.6% (t-test) and 78.3% (SHAP) accuracy.

Use of chitosan wound dressing for the treatment of surgical site infection: a case report.

Surgical site infections (SSIs) are treated using topical antiseptics and systemic antibiotics, but some cases are unresponsive to such regimens. This case study reports the effective healing of an SSI by a chitosan wound dressing (MaxioCel; Axio Biosolutions Private Limited, India) in a 63-year-old female patient. The patient presented with an infected, hard-to-heal wound in the abdominal region, developed after a hernia surgery, and was initially treated with standard procedures. However, due to the continuous progression of infection, a highly absorbent, bioactive microfibre dressing was selected for the treatment and was continued for two months with alternate-day dressing changes. After 60 days of treatment, wound healing was observed, along with remission from the infection, as well as reduction in exudate level and pain. The use of chitosan wound dressing in management of hard-to-heal infected wounds provides efficient remission of SSI and a faster healing rate.

Hashimoto's Thyroiditis Effects on Papillary Thyroid Carcinoma Outcomes: A Systematic Review.

Papillary thyroid carcinoma (PTC) and Hashimoto's thyroiditis (HT), also known as chronic lymphocytic thyroiditis, are both common thyroid diseases that are increasing in prevalence. PTC is well-differentiated cancer that generally has an excellent prognosis. HT is an autoimmune disease that often leads to hypothyroidism. A significant proportion of PTC patients also have HT. This systematic review will analyze the effect of HT on the characteristics and outcomes of PTC. Several databases were systematically searched using relevant medical subject headings (MeSH) keywords and phrases examining the connection between PTC and HT and the effect of their coexistence. Inclusion and exclusion criteria were applied, followed by quality appraisal. After that filtration process, 23 articles were selected with a total of 41,646 patients. Out of 22 studies commenting on tumor size, 12 studies demonstrated it to be smaller in HT patients, while 10 studies observed no effect. Eleven studies examined PTC angioinvasion, most of which found no difference in HT and non-HT patients. However, two studies found angioinvasion to be reduced in PTC patients. As for capsular infiltration, out of the five studies commenting on it, two found decreased occurrence, one found increased occurrence, and two had no difference. Extrathyroidal extension was found to be reduced in seven studies out of the 14 that examined it. Six other studies saw no effect. One study found increased extrathyroidal extension incidence overall, and another found it to be the case in patients younger than 45 years of age. Lymph node metastases were found to be reduced in several studies, while others found no difference. One study found increased central lymph node metastases in HT patients. As for prognoses, most studies found positive aspects. One study found an increased recurrence rate in HT patients, however, it did not have a relationship with deaths. In conclusion, when managing HT or HT and PTC patients, HT patients should be monitored closely for suspicious nodules due to their frequent co-occurrence. Although the effect of HT on PTC has been shown to be mostly protective, multifocality is more common in those patients and, therefore, a total thyroidectomy should be favored. The high false positive rates of lymph node metastases in diagnostic methods should be kept in mind when considering lymph node dissection. Additional diagnostic procedures such as frozen section histology should be considered for verification.

Persistent Periodic Uplink Scheduling Algorithm for Massive NB-IoT Devices.

Narrowband Internet of Things (NB-IoT) is one of the low-power wide-area network (LPWAN) technologies that aim to support enormous connections, featuring wide-area coverage, low power consumption, and low costs. NB-IoT could serve a massive number of IoT devices, but with very limited radio resources. Therefore, how to enable a massive number of IoT devices to transmit messages periodically, and with low latency, according to transmission requirements, has become the most crucial issue of NB-IoT. Moreover, IoT devices are designed to minimize power consumption so that the device battery can last for a long time. Similarly, the NB-IoT system must configure different power-saving mechanisms for different types of devices to prolong their battery lives. In this study, we propose a persistent periodic uplink scheduling algorithm (PPUSA) to assist a plethora of Internet of Things (IoT) devices in reporting their sensing data based on their sensing characteristics. PPUSA explicitly considers the power-saving mode and connection suspend/resume procedures to reduce the IoT device's power consumption and processing overhead. PPUSA allocates uplink resource units to IoT devices systematically so that it can support the periodic-uplink transmission of a plethora of IoT devices while maintaining low transmission latency for bursty data. The simulation results show that PPUSA can support up to 600,000 IoT devices when the NB-IoT uplink utilization is 80%. In addition, it takes only one millisecond for the transmission of the bursty messages.

The Need for Artificial Intelligence Based Risk Factor Analysis for Age-Related Macular Degeneration: A Review.

In epidemiology, a risk factor is a variable associated with increased disease risk. Understanding the role of risk factors is significant for developing a strategy to improve global health. There is strong evidence that risk factors like smoking, alcohol consumption, previous cataract surgery, age, high-density lipoprotein (HDL) cholesterol, BMI, female gender, and focal hyper-pigmentation are independently associated with age-related macular degeneration (AMD). Currently, in the literature, statistical techniques like logistic regression, multivariable logistic regression, etc., are being used to identify AMD risk factors by employing numerical/categorical data. However, artificial intelligence (AI) techniques have not been used so far in the literature for identifying risk factors for AMD. On the other hand, artificial intelligence (AI) based tools can anticipate when a person is at risk of developing chronic diseases like cancer, dementia, asthma, etc., in providing personalized care. AI-based techniques can employ numerical/categorical and/or image data thus resulting in multimodal data analysis, which provides the need for AI-based tools to be used for risk factor analysis in ophthalmology. This review summarizes the statistical techniques used to identify various risk factors and the higher benefits that AI techniques provide for AMD-related disease prediction. Additional studies are required to review different techniques for risk factor identification for other ophthalmic diseases like glaucoma, diabetic macular edema, retinopathy of prematurity, cataract, and diabetic retinopathy.

Utilization of Cerebrospinal Fluid Proteome Analysis in the Diagnosis of Meningioma: A Systematic Review.

Meningiomas have been classified as the most commonly occurring primary brain tumors. Although the majority of meningiomas are benign and slow-progressing, the tumors that grow to a larger size are associated with various risks during surgical procedures. Early detection of meningiomas is crucial to the treatment as those detected early can be treated through non-invasive methods. Due to their benign nature, meningiomas contain homogeneous protein biomarkers that can be easily identified. Cerebrospinal fluid (CSF) has a high protein composition which can be used to diagnose various brain tumors. Because CSF comes into direct contact with the brain during its functioning, it is one of the factors that makes it an important source of different biomarkers. An analysis of biochemical changes occurring in the CSF can be useful in assessing the condition of the periventricular white matter and the parenchyma. In this review, PubMed, Medline, PubMed Central, and Google Scholar were used to identify studies discussing meningiomas regarding their assessment, types, diagnosis, and treatment, with more attention directed towards the application of CSF proteome analysis in diagnosis. Priority was given to studies published within the last 15 years. The following keywords were used in the literature search: "cerebrospinal fluid," "meningiomas," "brain tumors," "primary brain tumors," "protein biomarkers," "proteome analysis," and "diagnosis." Subsequently, the 15 most relevant studies were selected for inclusion in the review. We excluded studies discussing different types of non-brain tumors as well as older articles. The selected studies also underwent a quality appraisal process using corresponding assessment tools. The selected articles were highly informative about meningiomas and the processes of diagnosis and treatment that are currently in use as well as those that are being developed or implemented. The use of CSF proteins in the diagnostic process is also discussed in this review. The studies also describe proteomics as a less invasive procedure that allows for the analysis of entire proteins and the projection of diagnostic images with higher resolutions that aid in the diagnosis.