Background and Objectives: Diabetic foot sepsis (DFS) accounts for approximately 60% of hospital admissions in patients with diabetes mellitus (DM). Individuals with DM are at risk of severe COVID-19. This study investigated factors associated with major amputation and mortality in patients admitted with DFS during the COVID-19 pandemic. Materials and Methods: Demographic information, COVID-19 and HIV status, clinical findings, laboratory results, treatment and outcome from records of patients with diabetic foot sepsis, were collected and analysed. Supervised machine learning algorithms were used to compare their ability to predict mortality due to diabetic foot sepsis. Results: Overall, 114 records were found and 57.9% (66/114) were of male patients. The mean age of the patients was 55.7 (14) years and 47.4% (54/114) and 36% (41/114) tested positive for COVID-19 and HIV, respectively. The median c-reactive protein was 168 mg/dl, urea 7.8 mmol/L and creatinine 92 µmol/L. The mean potassium level was 4.8 ± 0.9 mmol, and glycosylated haemoglobin 11.2 ± 3%. The main outcomes included major amputation in 69.3% (79/114) and mortality of 37.7% (43/114) died. AI. The levels of potassium, urea, creatinine and HbA1c were significantly higher in the deceased. Conclusions: The COVID-19 pandemic led to an increase in the rate of major amputation and mortality in patients with DFS. The in-hospital mortality was higher in patients above 60 years of age who tested positive for COVID-19. The Random Forest algorithm of ML can be highly effective in predicting major amputation and death in patients with DFS.

Background/Objective: Medullary thyroid carcinoma (MTC) is a rare yet aggressive form of thyroid cancer comprising a disproportionate share of thyroid cancer-related mortalities, despite its low prevalence. MTC differs from other differentiated thyroid malignancies due to its heterogeneous nature, presenting complexities in both hereditary and sporadic cases. Traditional management guidelines, which are designed primarily for papillary thyroid carcinoma (PTC), fall short in providing the individualized care required for patients with MTC. In recent years, the sheer volume of data generated from clinical evaluations, radiological imaging, pathological assessments, genetic mutations, and immunological profiles has made it humanly impossible for clinicians to simultaneously analyze and integrate these diverse data streams effectively. This data deluge necessitates the adoption of advanced technologies to assist in decision-making processes. Holomics, which is an integrated approach that combines various omics technologies, along with artificial intelligence (AI), emerges as a powerful solution to address these challenges. Methods: This article reviews how AI-driven precision oncology can enhance the diagnostic workup, staging, risk stratification, management, and follow-up care of patients with MTC by processing vast amounts of complex data quickly and accurately. Articles published in English language and indexed in Pubmed were searched. Results: AI algorithms can identify patterns and correlations that may not be apparent to human clinicians, thereby improving the precision of personalized treatment plans. Moreover, the implementation of AI in the management of MTC enables the collation and synthesis of clinical experiences from across the globe, facilitating a more comprehensive understanding of the disease and its treatment outcomes. Conclusions: The integration of holomics and AI in the management of patients with MTC represents a significant advancement in precision oncology. This innovative approach not only addresses the complexities of a rare and aggressive disease but also paves the way for global collaboration and equitable healthcare solutions, ultimately transforming the landscape of treatment and care of patients with MTC. By leveraging AI and holomics, we can strive toward making personalized healthcare accessible to every individual, regardless of their economic status, thereby improving overall survival rates and quality of life for MTC patients worldwide. This global approach aligns with the United Nations Sustainable Development Goal 3, which aims to ensure healthy lives and promote well-being at all ages.

Background/Objective: Prevention of foot ulceration is critical to reduce the amputation rate in individuals with diabetes mellitus (DM). We investigated the knowledge of diabetic foot care management and prevention of diabetic foot ulcer (DFU) among categories of healthcare practitioners (HCPs). Methods: This descriptive cross-sectional observational study was conducted at public healthcare facilities within the Charlotte Maxeke Johannesburg Academic Hospital Cluster in Gauteng, South Africa. Participants included podiatrists, physiotherapists, occupational therapists, dieticians, medical orthotists and prosthetists, nurses, and medical doctors. Data were collected using a self-administered questionnaire that assessed knowledge of DFU risk factors, foot examination, foot care, and appropriate footwear. Knowledge level was classified as extremely poor if less than 50% of participants from a category of HCPs answered appropriately, reasonable for 50–59%, average at 60–69%, above average from 70–79%, and excellent when ≥80%. The questionnaire was completed by 449 HCPs and analyzed using STATA version 15, with statistical significance set at a p-value less than 0.05. Results: A total of 449 HCPs participated, which comprised the following: 48.1% (216) therapeutic health practitioners (THPs), 37.4% (168) nurses, and 14.5% (65) medical doctors. Only 36% (162) of participants had prior education on DFU. The overall knowledge levels among participants were risk factors of DFU (80%), foot examination (80%), identification of limb-threatening conditions (82%), foot care (77%), and footwear (65%). Medical doctors had the highest median scores for risk factors (85) and foot examination (80), followed by nurses (80 for risk factors and 78 for foot examination). THPs had the lowest median scores for risk factors (78) and foot examinations (70). Differences in knowledge levels across HCPs were statistically significant (p < 0.05). Conclusions: THPs have insufficient knowledge of diabetic foot care and prevention of DFUs. Comprehensive training and targeted educational programs are needed to fill these gaps and improve patient care.

Background: Diabetic foot sepsis (DFS) accounts for approximately 60% of hospital admissions in patients with diabetes mellitus (DM). Individuals with DM are at risk of severe COVID-19. This study investigated investigated factors associated with major amputation and mortality in patients admitted with DFS during the COVID-19 pandemic. Methods: Demographic information, COVID-19 and HIV status, clinical findings, laboratory results, treatment and outcome from records of patients with diabetic foot sepsis. The chi-square test, Fisher’s test, Student t-test or Mann-Whitney compare findings when appropriate. Multivariate logistic regression to determine factors that were associated with major amputation and mortality. Results of logistic regression were compared with outputs from analysis using machine learning algorithms. Kaplan-Meier survival curve to compare survival between COVID-19 positive and negative patients. Statistical was a p-value below 0.05. Supervised machine learning algorithms were used to compare their ability to predict major and deaths. Results: Overall, 114 records were found and 57.9% (66/114) were of male patients. The mean age of the patients was 55.7 (14) years and 47.4% (54/114) and 36% (41/114) tested positive for COVID-19 and HIV, respectively. The median c-reactive protein was 168mg/dl, urea 7.8mmol/l and creatinine 92µmol/l. The mean potassium level was 4.8±0.9mmol, and glycosylated haemoglobin 11.2±3%. The main outcomes included major amputation in 69.3% (79/114) and mortality of 37.7% (43/114) died. AI. The level of potassium, urea, creatinine and HbA1c were significantly higher in the deceased. Conclusions: The COVID-19 pandemic led to an increase in the rate of major amputation and mortality in patients with DFS. The in-hospital mortality was higher in patients above 60 years of age who tested positive for COVID-19. The Random Forest algorithm of ML can be highly effective predicting major amputation and death in patients with DFS.

Background/Objective: Prevention of foot ulceration is critical to reduce the rate of amputation in individuals with diabetes mellitus (DM). We investigated knowledge of risk factors and prevention of diabetic foot ulcer (DFU) among healthcare practitioners (HCPs). Methods: This was a prospective cross-sectional observational study using a self-administered questionnaire. Participants were HCPs involved in management of patients with DM. The questionnaire investigated professional background, prior education, knowledge of risk factors DFU, foot care and appropriate footwear. Participants were asked to indicate if they agreed or disagree with a statement. Scores were based on percentage response by each category of HCPs. Knowledge level was classified as very poor if less than 50% of participants from a category of HCPs answered appropriately, reasonable for 50%-59%, average at 60%-69%, above average from 70%-79% and excellent when ≥80%. The chi-square test to compare the knowledge levels across the categories of HCPs. Results: 449 HCPs participated and 48.1% (216/449) were therapeutic health practitioners (THPs), 37.4% (168/449) nurses and 14.5% (65/449) medical doctors. 36% (162/449) of participants had prior education on DFU. Overall knowledge level among participants of risk factors of DFU was 80%, appropriate technique of foot examination 80%, identification of limb-threatening conditions 82%, proper foot care 77% and selection of appropriate footwear 65%. Differences in knowledge levels across HPCs was statistically significant (P <0.05). Conclusion: Majority of HCPs had no prior education on prevention of DFU. The level of knowledge regarding foot care, risk factors and prevention of DFU among HCPs was mostly insufficient.

Medullary thyroid carcinoma (MTC) is a rare yet aggressive form of thyroid cancer, comprising a disproportionate share of thyroid cancer-related mortalities despite its low prevalence. MTC differs from other differentiated thyroid malignancies due to its heterogeneous nature, presenting complexities in both hereditary and sporadic cases. Traditional management guidelines, designed primarily for papillary thyroid carcinoma (PTC), fall short in providing the individualized care required for MTC patients. In recent years, the sheer volume of data generated from clinical evaluations, radiological imaging, pathological assessments, genetic mutations, and immunological profiles has made it humanly impossible for clinicians to simultaneously analyze and integrate these diverse data streams effectively. This data deluge necessitates the adoption of advanced technologies to assist in decision-making processes. Holomics, an integrated approach that combines various omics technologies, along with artificial intelligence (AI), emerges as a powerful solution to address these challenges. AI-driven precision oncology can significantly enhance the diagnostic work-up, staging, risk stratification, management, and follow-up care of MTC patients by processing vast amounts of complex data quickly and accurately. AI algorithms can identify patterns and correlations that may not be apparent to human clinicians, thereby improving the precision of personalized treatment plans. Moreover, the implementation of AI in MTC management enables the collation and synthesis of clinical experiences from across the globe, facilitating a more comprehensive understanding of the disease and its treatment outcomes. This global approach aligns with the United Nations Sustainable Development Goal 3, which aims to ensure healthy lives and promote well-being for all at all ages. By leveraging AI and holomics, we can strive towards making personalized healthcare accessible to every individual, regardless of their economic status, thereby improving overall survival rates and quality of life for MTC patients worldwide. In conclusion, the integration of holomics and AI in the management of medullary thyroid carcinoma represents a significant advancement in precision oncology. This innovative approach not only addresses the complexities of a rare and aggressive disease but also paves the way for global collaboration and equitable healthcare solutions, ultimately transforming the landscape of MTC treatment and care.