Artificial intelligence for cardiovascular disease risk assessment in personalised framework: a scoping review.

Background: The field of precision medicine endeavors to transform the healthcare industry by advancing individualised strategies for diagnosis, treatment modalities, and predictive assessments. This is achieved by utilizing extensive multidimensional biological datasets encompassing diverse components, such as an individual's genetic makeup, functional attributes, and environmental influences. Artificial intelligence (AI) systems, namely machine learning (ML) and deep learning (DL), have exhibited remarkable efficacy in predicting the potential occurrence of specific cancers and cardiovascular diseases (CVD). Methods: We conducted a comprehensive scoping review guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. Our search strategy involved combining key terms related to CVD and AI using the Boolean operator AND. In August 2023, we conducted an extensive search across reputable scholarly databases including Google Scholar, PubMed, IEEE Xplore, ScienceDirect, Web of Science, and arXiv to gather relevant academic literature on personalised medicine for CVD. Subsequently, in January 2024, we extended our search to include internet search engines such as Google and various CVD websites. These searches were further updated in March 2024. Additionally, we reviewed the reference lists of the final selected research articles to identify any additional relevant literature. Findings: A total of 2307 records were identified during the process of conducting the study, consisting of 564 entries from external sites like arXiv and 1743 records found through database searching. After 430 duplicate articles were eliminated, 1877 items that remained were screened for relevancy. In this stage, 1241 articles remained for additional review after 158 irrelevant articles and 478 articles with insufficient data were removed. 355 articles were eliminated for being inaccessible, 726 for being written in a language other than English, and 281 for not having undergone peer review. Consequently, 121 studies were deemed suitable for inclusion in the qualitative synthesis. At the intersection of CVD, AI, and precision medicine, we found important scientific findings in our scoping review. Intricate pattern extraction from large, complicated genetic datasets is a skill that AI algorithms excel at, allowing for accurate disease diagnosis and CVD risk prediction. Furthermore, these investigations have uncovered unique genetic biomarkers linked to CVD, providing insight into the workings of the disease and possible treatment avenues. The construction of more precise predictive models and personalised treatment plans based on the genetic profiles of individual patients has been made possible by the revolutionary advancement of CVD risk assessment through the integration of AI and genomics. Interpretation: The systematic methodology employed ensured the thorough examination of available literature and the inclusion of relevant studies, contributing to the robustness and reliability of the study's findings. Our analysis stresses a crucial point in terms of the adaptability and versatility of AI solutions. AI algorithms designed in non-CVD domains such as in oncology, often include ideas and tactics that might be modified to address cardiovascular problems. Funding: No funding received.

Guidelines on offloading foot ulcers in persons with diabetes (IWGDF 2023 update).

AIMS: Offloading mechanical tissue stress is arguably the most important of multiple interventions needed to heal diabetes-related foot ulcers. This is the 2023 International Working Group on the Diabetic Foot (IWGDF) evidence-based guideline on offloading interventions to promote healing of foot ulcers in persons with diabetes. It serves as an update of the 2019 IWGDF guideline. MATERIALS AND METHODS: We followed the GRADE approach by devising clinical questions and important outcomes in the PICO (Patient-Intervention-Control-Outcome) format, undertaking a systematic review and meta-analyses, developing summary of judgement tables and writing recommendations and rationales for each question. Each recommendation is based on the evidence found in the systematic review, expert opinion where evidence was not available, and a careful weighing of GRADE summary of judgement items including desirable and undesirable effects, certainty of evidence, patient values, resources required, cost effectiveness, equity, feasibility, and acceptability. RESULTS: For healing a neuropathic plantar forefoot or midfoot ulcer in a person with diabetes, use a non-removable knee-high offloading device as the first-choice offloading intervention. If contraindications or patient intolerance to non-removable offloading exist, consider using a removable knee-high or ankle-high offloading device as the second-choice offloading intervention. If no offloading devices are available, consider using appropriately fitting footwear combined with felted foam as the third-choice offloading intervention. If such a non-surgical offloading treatment fails to heal a plantar forefoot ulcer, consider an Achilles tendon lengthening, metatarsal head resection, joint arthroplasty, or metatarsal osteotomy. For healing a neuropathic plantar or apex lesser digit ulcer secondary to flexibile toe deformity, use digital flexor tendon tenotomy. For healing rearfoot, non-plantar or ulcers complicated with infection or ischaemia, further recommendations have been outlined. All recommendations have been summarised in an offloading clinical pathway to help facilitate the implementation of this guideline into clinical practice. CONCLUSION: These offloading guideline recommendations should help healthcare professionals provide the best care and outcomes for persons with diabetes-related foot ulcers and reduce the person's risk of infection, hospitalisation and amputation.

Imperatorin from the aerial parts of Cleome viscosa L.: a characterization study and evaluation of the antibacterial activity.

Cleome viscosa L., a member of the family Cleomaceae, is a potential medicinal plant, known for several bioactive properties such as: anticancer, antidiabetic, antioxidant, anti-inflammatory, antimicrobial, wound healing, etc. Our study aimed to isolate a bioactive compound and assess its antibacterial activity. The crystal compound imperatorin was isolated and reported for the first time from the aerial parts of C. viscosa. The isolation was made using silica gel (100-200 mesh) column chromatography. The structure of imperatorin was investigated through single-crystal XRD, unit cell molecules, FTIR, and ESI-MS spectral analysis. The results validated imperatorin's triclinic crystal structure and P2i/c distance group. The electronic structure was also calculated (4.28/6.21 D) along with the frontier molecular orbital, dipole moment, atomic charges, and electrostatic map of particles in gaseous stage and active site. Imperatorin showed highest activity at 40 µg/mL concentration against Gram + ve bacteria: Staphylococcus aureus (3 ± 0.2 mm), Bacillus subtilis (3 ± 0.6 mm), and Gram -ve bacteria: Klebsiella pneumoniae (3 ± 0.2 mm), Escherichia coli (5 ± 0.2 mm). The study highlights that the compound can be isolated in larger quantities as the plant is easily available across the tropics.

Molecular and pro-inflammatory aspects of COVID-19: The impact on cardiometabolic health.

Obesity, type 2 diabetes (T2DM), hypertension (HTN), and Cardiovascular Disease (CVD) often cluster together as "Cardiometabolic Disease" (CMD). Just under 50% of patients with CMD increased the risk of morbidity and mortality right from the beginning of the COVID-19 pandemic as it has been reported in most countries affected by the SARS-CoV2 virus. One of the pathophysiological hallmarks of COVID-19 is the overactivation of the immune system with a prominent IL-6 response, resulting in severe and systemic damage involving also cytokines such as IL2, IL4, IL8, IL10, and interferon-gamma were considered strong predictors of COVID-19 severity. Thus, in this mini-review, we try to describe the inflammatory state, the alteration of the adipokine profile, and cytokine production in the obese state of infected and not infected patients by SARS-CoV2 with the final aim to find possible influences of COVID-19 on CMD and CVD. The immunological-based discussion of the molecular processes could inspire the study of promising targets for managing CMD patients and its complications during COVID-19.

Cardiovascular risk in newly diagnosed type 2 diabetes patients in India.

BACKGROUND: Type 2 diabetes mellitus (T2DM) worldwide continues to increase, in particular in India. Early T2DM diagnosis followed by appropriate management will result in more cardiovascular event free life years. However, knowledge of the cardiovascular profile of newly diagnosed T2DM patients is still limited. The aim of this study was to understand the extent of cardiovascular disease (CVD) risk of newly diagnosed T2DM patients in India. METHODS: A cross sectional observational study was conducted to evaluate clinical laboratory and socio-demographic parameters of 5,080 newly diagnosed T2DM patients (48.3 ± 12.8 years of age; 36.7% female). In addition, we determined their cardiovascular risk according to the guidelines of the Lipid Association of India (LAI) and the criteria of the QRISK3 score. RESULTS: Of the newly T2DM diagnosed patients in India 2,007(39.5%) were classified as "High risk" and 3,073 (60.5%) were classified as "Very high risk" based on LAI criteria. On average, patients had 1.7 ± 0.9 major atherosclerotic cardiovascular disease (ASCVD) risk factors. Low HDL-C value was the most frequent major risk (2,823; 55.6%) followed by high age (2,502; 49.3%), hypertension (2,141; 42.1%), smoking/tobacco use (1,078; 21.2%) and chronic kidney disease stage 3b or higher (568; 11.2%). In addition, 4,192 (82.5%) patients appeared to have at least one cholesterol abnormality and, if the latest LAI recommendations are applied, 96.5% (4,902) presented with lipid values above recommended targets. Based on the QRISK3 calculation Indian diabetes patients had an average CVD risk of 15.3 ± 12.3%, (12.2 ± 10.1 vs. 17.1 ± 13.5 [p<0.001] for females and males, respectively). CONCLUSIONS: Newly diagnosed Indian T2DM patients are at high ASCVD risk. Our data therefore support the notion that further extension of nationwide ASCVD risk identification programs and prevention strategies to reduce the occurrence of cardiovascular diseases are warranted.

Serum levels of novel anti-inflammatory cytokine Interleukin-38 in diabetes patients infected with latent tuberculosis (DM-LTB-3).

IL-38 is a recently discovered, novel anti-inflammatory cytokine, which belongs to the IL-1ß family. The role played by this cytokine in diabetes-tuberculosis nexus is not known. Serum levels of IL-38, TNF-α, IL-6, and IL-1ß in Normal Glucose Tolerance (NGT) and chronic Diabetes (DM) subjects, both with and without latent tuberculosis (LTB) (n = 256) were quantified by ELISA. While, serum levels of IL-38 were significantly reduced, the levels of TNF-α, IL-6, and IL-1ß were not altered, in LTB infected diabetes patients. While no significant secretion of IL-38 was detected in the quantiferon supernatant, secretion of TNF-α, IL-6, and IL-1ß was significantly reduced in LTB infected diabetes patients. The decreased systemic levels of IL-38 and reduced in vitro secretion of other pro-inflammatory cytokines might represent a crucial pathway associated with diabetes-tuberculosis nexus.

Baseline IL-6 is a biomarker for unfavourable tuberculosis treatment outcomes: a multisite discovery and validation study.

BACKGROUND: Biomarkers of unfavourable tuberculosis (TB) treatment outcomes are needed to accelerate new drug and regimen development. Whether plasma cytokine levels can predict unfavourable TB treatment outcomes is unclear. METHODS: We identified and internally validated the association between 20 a priori selected plasma inflammatory markers and unfavourable treatment outcomes of failure, recurrence and all-cause mortality among adults with drug-sensitive pulmonary TB in India. We externally validated these findings in two independent cohorts of predominantly diabetic and HIV co-infected TB patients in India and South Africa, respectively. RESULTS: Pre-treatment interferon-γ, interleukin (IL)-13 and IL-6 were associated with treatment failure in the discovery analysis. Internal validation confirmed higher pre-treatment IL-6 concentrations among failure cases compared with controls. External validation among predominantly diabetic TB patients found an association between pre-treatment IL-6 concentrations and subsequent recurrence and death. Similarly, external validation among predominantly HIV co-infected TB patients found an association between pre-treatment IL-6 concentrations and subsequent treatment failure and death. In a pooled analysis of 363 TB cases from the Indian and South African validation cohorts, high pre-treatment IL-6 concentrations were associated with higher risk of failure (adjusted OR (aOR) 2.16, 95% CI 1.08-4.33; p=0.02), recurrence (aOR 5.36, 95% CI 2.48-11.57; p<0.001) and death (aOR 4.62, 95% CI 1.95-10.95; p<0.001). Adding baseline IL-6 to a risk prediction model comprised of low body mass index, high smear grade and cavitation improved model performance by 15% (C-statistic 0.66 versus 0.76; p=0.02). CONCLUSIONS: Pre-treatment IL-6 is a biomarker for unfavourable TB treatment outcomes. Future studies should identify optimal IL-6 concentrations for point-of-care risk prediction.

Six artificial intelligence paradigms for tissue characterisation and classification of non-COVID-19 pneumonia against COVID-19 pneumonia in computed tomography lungs.

BACKGROUND: COVID-19 pandemic has currently no vaccines. Thus, the only feasible solution for prevention relies on the detection of COVID-19-positive cases through quick and accurate testing. Since artificial intelligence (AI) offers the powerful mechanism to automatically extract the tissue features and characterise the disease, we therefore hypothesise that AI-based strategies can provide quick detection and classification, especially for radiological computed tomography (CT) lung scans. METHODOLOGY: Six models, two traditional machine learning (ML)-based (k-NN and RF), two transfer learning (TL)-based (VGG19 and InceptionV3), and the last two were our custom-designed deep learning (DL) models (CNN and iCNN), were developed for classification between COVID pneumonia (CoP) and non-COVID pneumonia (NCoP). K10 cross-validation (90% training: 10% testing) protocol on an Italian cohort of 100 CoP and 30 NCoP patients was used for performance evaluation and bispectrum analysis for CT lung characterisation. RESULTS: Using K10 protocol, our results showed the accuracy in the order of DL > TL > ML, ranging the six accuracies for k-NN, RF, VGG19, IV3, CNN, iCNN as 74.58 ± 2.44%, 96.84 ± 2.6, 94.84 ± 2.85%, 99.53 ± 0.75%, 99.53 ± 1.05%, and 99.69 ± 0.66%, respectively. The corresponding AUCs were 0.74, 0.94, 0.96, 0.99, 0.99, and 0.99 (p-values < 0.0001), respectively. Our Bispectrum-based characterisation system suggested CoP can be separated against NCoP using AI models. COVID risk severity stratification also showed a high correlation of 0.7270 (p < 0.0001) with clinical scores such as ground-glass opacities (GGO), further validating our AI models. CONCLUSIONS: We prove our hypothesis by demonstrating that all the six AI models successfully classified CoP against NCoP due to the strong presence of contrasting features such as ground-glass opacities (GGO), consolidations, and pleural effusion in CoP patients. Further, our online system takes < 2 s for inference.

Low-Cost Office-Based Cardiovascular Risk Stratification Using Machine Learning and Focused Carotid Ultrasound in an Asian-Indian Cohort.

This study developed an office-based cardiovascular risk calculator using a machine learning (ML) algorithm that utilized a focused carotid ultrasound. The design of this study was divided into three steps. The first step involved collecting 18 office-based biomarkers consisting of six clinical risk factors (age, sex, body mass index, systolic blood pressure, diastolic blood pressure, and smoking) and 12 carotid ultrasound image-based phenotypes. The second step consisted of the design of an ML-based cardiovascular risk calculator-called "AtheroEdge Composite Risk Score 2.0" (AECRS2.0ML) for risk stratification, considering chronic kidney disease (CKD) as the surrogate endpoint of cardiovascular disease. The last step consisted of comparing AECRS2.0ML against the currently utilized office-based CVD calculators, namely the Framingham risk score (FRS) and the World Health Organization (WHO) risk scores. A cohort of 379 Asian-Indian patients with type-2 diabetes mellitus, hypertension, and chronic kidney disease (stage 1 to 5) were recruited for this cross-sectional study. From this retrospective cohort, 758 ultrasound scan images were acquired from the far walls of the left and right common carotid arteries [mean age = 55 ± 10.8 years, 67.28% males, 91.82% diabetic, 86.54% hypertensive, and 83.11% with CKD]. The mean office-based cardiovascular risk estimates using FRS and WHO calculators were 26% and 19%, respectively. AECRS2.0ML demonstrated a better risk stratification ability having a higher area-under-the-curve against FRS and WHO by ~30% (0.871 vs. 0.669) and ~ 20% (0.871 vs. 0.727), respectively. The office-based machine-learning cardiovascular risk-stratification tool (AECRS2.0ML) shows superior performance compared to currently available conventional cardiovascular risk calculators.

3-D optimized classification and characterization artificial intelligence paradigm for cardiovascular/stroke risk stratification using carotid ultrasound-based delineated plaque: Atheromatic™ 2.0.

BACKGROUND AND PURPOSE: Atherosclerotic plaque tissue rupture is one of the leading causes of strokes. Early carotid plaque monitoring can help reduce cardiovascular morbidity and mortality. Manual ultrasound plaque classification and characterization methods are time-consuming and can be imprecise due to significant variations in tissue characteristics. We report a novel artificial intelligence (AI)-based plaque tissue classification and characterization system. METHODS: We hypothesize that symptomatic plaque is hypoechoic due to its large lipid core and minimal collagen, as well as its heterogeneous makeup. Meanwhile, asymptomatic plaque is hyperechoic due to its small lipid core, abundant collagen, and the fact that it is often calcified. We designed a computer-aided diagnosis (CADx) system consisting of three kinds of deep learning (DL) classification paradigms: Deep Convolutional Neural Network (DCNN), Visual Geometric Group-16 (VGG16), and transfer learning, (tCNN). DCNN was 3-D optimized by varying the number of CNN layers and data augmentation frameworks. The DL systems were benchmarked against four types of machine learning (ML) classification systems, and the CADx system was characterized using two novel strategies consisting of DL mean feature strength (MFS) and a bispectrum model using higher-order spectra. RESULTS: After balancing symptomatic and asymptomatic plaque classes, a five-fold augmentation process was applied, yielding 1000 carotid scans in each class. Then, using a K10 protocol (trained to test the ratio of 90%-10%), tCNN and DCNN yielded accuracy (area under the curve (AUC)) pairs of 83.33%, 0.833 (p < 0.0001) and 95.66%, 0.956 (p < 0.0001), respectively. DCNN was superior to ML by 7.01%. As part of the characterization process, the MFS of the symptomatic plaque was found to be higher compared to the asymptomatic plaque by 17.5% (p < 0.0001). A similar pattern was seen in the bispectrum, which was higher for symptomatic plaque by 5.4% (p < 0.0001). It took <2 s to perform the online CADx process on a supercomputer. CONCLUSIONS: The performance order of the three AI systems was DCNN > tCNN > ML. Bispectrum-based on higher-order spectra proved a powerful paradigm for plaque tissue characterization. Overall, the AI-based systems offer a powerful solution for plaque tissue classification and characterization.

Integration of estimated glomerular filtration rate biomarker in image-based cardiovascular disease/stroke risk calculator: a south Asian-Indian diabetes cohort with moderate chronic kidney disease.

BACKGROUND: Recently, a 10-year image-based integrated calculator (called AtheroEdge Composite Risk Score-AECRS1.0) was developed which combines conventional cardiovascular risk factors (CCVRF) with image phenotypes derived from carotid ultrasound (CUS). Such calculators did not include chronic kidney disease (CKD)-based biomarker called estimated glomerular filtration rate (eGFR). The novelty of this study is to design and develop an advanced integrated version called-AECRS2.0 that combines eGFR with image phenotypes to compute the composite risk score. Furthermore, AECRS2.0 was benchmarked against QRISK3 which considers eGFR for risk assessment. METHODS: The method consists of three major steps: 1) five, current CUS image phenotypes (CUSIP) measurements using AtheroEdge system (AtheroPoint, CA, USA) consisting of: average carotid intima-media thickness (cIMTave), maximum cIMT (cIMTmax), minimum cIMT (cIMTmin), variability in cIMT (cIMTV), and total plaque area (TPA); 2) five, 10-year CUSIP measurements by combining these current five CUSIP with 11 CCVRF (age, ethnicity, gender, body mass index, systolic blood pressure, smoking, carotid artery type, hemoglobin, low-density lipoprotein cholesterol, total cholesterol, and eGFR); 3) AECRS2.0 risk score computation and its comparison to QRISK3 using area-under-the-curve (AUC). RESULTS: South Asian-Indian 339 patients were retrospectively analyzed by acquiring their left/right common carotid arteries (678 CUS, mean age: 54.25±9.84 years; 75.22% males; 93.51% diabetic with HbA1c ≥6.5%; and mean eGFR 73.84±20.91 mL/min/1.73m2). The proposed AECRS2.0 reported higher AUC (AUC=0.89, P<0.001) compared to QRISK3 (AUC=0.51, P<0.001) by ~74% in CKD patients. CONCLUSIONS: An integrated calculator AECRS2.0 can be used to assess the 10-year CVD/stroke risk in patients suffering from CKD. AECRS2.0 was much superior to QRISK3.

Effectiveness of offloading interventions to heal foot ulcers in persons with diabetes: a systematic review.

BACKGROUND: Offloading interventions are commonly used in clinical practice to heal foot ulcers. The aim of this updated systematic review is to investigate the effectiveness of offloading interventions to heal diabetic foot ulcers. METHODS: We updated our previous systematic review search of PubMed, EMBASE, and Cochrane databases to also include original studies published between July 29, 2014 and August 13, 2018 relating to four offloading intervention categories in populations with diabetic foot ulcers: (a) offloading devices, (b) footwear, (c) other offloading techniques, and (d) surgical offloading techniques. Outcomes included ulcer healing, plantar pressure, ambulatory activity, adherence, adverse events, patient-reported measures, and cost-effectiveness. Included controlled studies were assessed for methodological quality and had key data extracted into evidence and risk of bias tables. Included non-controlled studies were summarised on a narrative basis. RESULTS: We identified 41 studies from our updated search for a total of 165 included studies. Six included studies were meta-analyses, 26 randomised controlled trials (RCTs), 13 other controlled studies, and 120 non-controlled studies. Five meta-analyses and 12 RCTs provided high-quality evidence for non-removable knee-high offloading devices being more effective than removable offloading devices and therapeutic footwear for healing plantar forefoot and midfoot ulcers. Total contact casts (TCCs) and non-removable knee-high walkers were shown to be equally effective. Moderate-quality evidence exists for removable knee-high and ankle-high offloading devices being equally effective in healing, but knee-high devices have a larger effect on reducing plantar pressure and ambulatory activity. Low-quality evidence exists for the use of felted foam and surgical offloading to promote healing of plantar forefoot and midfoot ulcers. Very limited evidence exists for the efficacy of any offloading intervention for healing plantar heel ulcers, non-plantar ulcers, and neuropathic ulcers with infection or ischemia. CONCLUSION: Strong evidence supports the use of non-removable knee-high offloading devices (either TCC or non-removable walker) as the first-choice offloading intervention for healing plantar neuropathic forefoot and midfoot ulcers. Removable offloading devices, either knee-high or ankle-high, are preferred as second choice over other offloading interventions. The evidence bases to support any other offloading intervention is still weak and more high-quality controlled studies are needed in these areas.

Guidelines on offloading foot ulcers in persons with diabetes (IWGDF 2019 update).

The International Working Group on the Diabetic Foot (IWGDF) has published evidence-based guidelines on the prevention and management of diabetic foot disease since 1999. This guideline is on the use of offloading interventions to promote the healing of foot ulcers in people with diabetes and updates the previous IWGDF guideline. We followed the GRADE methodology to devise clinical questions and critically important outcomes in the PICO format, to conduct a systematic review of the medical-scientific literature, and to write recommendations and their rationale. The recommendations are based on the quality of evidence found in the systematic review, expert opinion where evidence was not available, and a weighing of the benefits and harms, patient preferences, feasibility and applicability, and costs related to the intervention. For healing a neuropathic plantar forefoot or midfoot ulcer in a person with diabetes, we recommend that a nonremovable knee-high offloading device is the first choice of offloading treatment. A removable knee-high and removable ankle-high offloading device are to be considered as the second- and third-choice offloading treatment, respectively, if contraindications or patient intolerance to nonremovable offloading exist. Appropriately, fitting footwear combined with felted foam can be considered as the fourth-choice offloading treatment. If non-surgical offloading fails, we recommend to consider surgical offloading interventions for healing metatarsal head and digital ulcers. We have added new recommendations for the use of offloading treatment for healing ulcers that are complicated with infection or ischaemia and for healing plantar heel ulcers. Offloading is arguably the most important of multiple interventions needed to heal a neuropathic plantar foot ulcer in a person with diabetes. Following these recommendations will help health care professionals and teams provide better care for diabetic patients who have a foot ulcer and are at risk for infection, hospitalization, and amputation.

Heterogeneity in the cytokine profile of tuberculosis - diabetes co-morbidity.

Tuberculosis - diabetes (TB-DM) co-morbidity is characterized by heterogeneity in clinical and biochemical parameters between newly diagnosed diabetic individuals with TB (TB-NDM) and known diabetic individuals at incident TB (TB-KDM). However, the immunological profile underlying this heterogeneity is not explored. To identify the cytokine profiles in TB-NDM and TB-KDM individuals, we examined the plasma cytokine levels as well as TB-antigen stimulated levels of pro-inflammatory cytokines. TB-KDM individuals exhibit significantly higher levels of IFNγ, IL-2, TNFα, IL-17A, IL-1α, IL-1ß and IL-6 in comparison to TB-NDM, TB alone and DM alone individuals. TB-NDM individuals are characterized by significantly lower levels of blood glucose and glycated hemoglobin in comparison to TB-KDM with both groups exhibiting a significant lowering of glycated hemoglobin levels at 6  months of anti-tuberculosis therapy (ATT). TB-NDM individuals are characterized by significantly diminished - unstimulated levels of IFNγ, IL-2, TNFα, IL-17A, IL-1α, IL-1ß and IL-12 at pre-treatment, of IFNγ, IL-2 and IL-1α at 2  months of ATT and IL-2 at post-treatment in comparison to TB-KDM. TB-NDM individuals are also characterized by significantly diminished TB-antigen stimulated levels of IFNγ, IL-2, TNFα, IL-17A, IL-17F, IL-1α, IL-1ß and/or IL-6 at pre-treatment and at 2  months of ATT and IFNγ, IL-2, IL-1α and IL-1ß at post-treatment. In addition, TB-NDM individuals are characterized by significantly diminished mitogen - stimulated levels of IL-17F and IL-6 at pre-treatment and IL-6 alone at 6 months of ATT. Therefore, our data reveal considerable heterogeneity in the immunological underpinnings of TB-DM co-morbidity. Our data also suggest that TB-NDM exhibits a characteristic profile, which is both biochemically and immunologically distinct from TB-KDM.

Gait changes in persons with diabetes: Early risk marker for diabetic foot ulcer.

Background Increasing prevalence of diabetic foot ulcer (DFU) and subsequent foot amputation in persons with type 2 diabetic neuropathy is a well known fact. The present study was aimed to identify the initial risk marker for DFU. Methods Dynamic plantar pressure analysis was done for persons with type 2 diabetes mellitus (T2DM) without neuropathy (D), patients with diabetic neuropathy (DN) with normal foot profile and healthy persons with normal foot profile (C). Results The data showed a significant difference in dynamic peak plantar pressure between C and DN (P = 0.035) and no significant difference between D and DN (P = 0.997). The dynamic segmental peak plantar pressure results showed significant difference only in the medial heel region (P = 0.009) among the three groups. Conclusions Gait variations and restrictions in subtalar and first metatarsophalangeal joint are found in persons with diabetic neuropathy even before the onset of foot deformity.

Efficacy of Recombinant Human Epidermal Growth Factor (Regen-D 150) in Healing Diabetic Foot Ulcers: A Hospital-Based Randomized Controlled Trial.

To validate the efficacy of recombinant human epidermal growth factor (hEGH) in healing diabetic foot ulcers (DFUs) at biochemical and molecular levels. A total of 50 noninfected DFU subjects were recruited for the study and divided into 2 groups based on the treatment application on the subjects. Group 1: DFU subjects treated with hEGH gel-based product called Regen-D 150 (n = 27) and group 2: DFU subjects treated with alternative placebo as the control group (n = 23). Patients were observed for 30 days and punch biopsy was taken at days 0 and 14. Histologic analysis was done to study the matrix alignment, cellular infiltration, and differentiation of epithelial layers. Biochemical analysis was done to quantitatively estimate the amount of collagen and proteoglycans regenerated in the wound area. Complete healing of ulcers was observed in 21 (78%) subjects in group 1, whereas only 12 (52%) subjects among group 2 reported of complete healing of ulcer after completion of the study period of 30 days. Collagen and fibroblasts were significantly developed in group 1 when observed in the follow-up samples. Healing time of the wound among the group 1 subjects was significantly less than the group 2 subjects (45 ± 12 vs 72 ± 18 days, P < .0001) and even showed a better blood glucose level. Early and regular application of the hEGH on DFUs will lead to prevention of leg amputations and would serve to act as a major treatment therapy for healing of chronic wounds.

Systemic RAGE ligands are upregulated in tuberculosis individuals with diabetes co-morbidity and modulated by anti-tuberculosis treatment and metformin therapy.

BACKGROUND: Ligands of the receptor for advanced glycation end products (RAGE) are key signalling molecules in the innate immune system but their role in tuberculosis-diabetes comorbidity (TB-DM) has not been investigated. METHODS: We examined the systemic levels of soluble RAGE (sRAGE), advanced glycation end products (AGE), S100A12 and high mobility group box 1 (HMGB1) in participants with either TB-DM, TB, DM or healthy controls (HC). RESULTS: Systemic levels of AGE, sRAGE and S100A12 were significantly elevated in TB-DM and DM in comparison to TB and HC. During follow up, AGE, sRAGE and S100A12 remained significantly elevated in TB-DM compared to TB at 2nd month and 6th month of anti-TB treatment (ATT). RAGE ligands were increased in TB-DM individuals with bilateral and cavitary disease. sRAGE and S100A12 correlated with glycated hemoglobin levels. Within the TB-DM group, those with known diabetes (KDM) revealed significantly increased levels of AGE and sRAGE compared to newly diagnosed DM (NDM). KDM participants on metformin treatment exhibited significantly diminished levels of AGE and sRAGE in comparison to those on non-metformin regimens. CONCLUSIONS: Our data demonstrate that RAGE ligand levels reflect disease severity and extent in TB-DM, distinguish KDM from NDM and are modulated by metformin therapy.

Plasma Eicosanoid Levels in Tuberculosis and Tuberculosis-Diabetes Co-morbidity Are Associated With Lung Pathology and Bacterial Burden.

Host eicosanoids are lipid mediators of inflammation that are commonly accepted as important modulators of the host immune response in Mycobacterium tuberculosis infection. During active tuberculosis (TB), eicosanoids may play an important role in the regulation of inflammatory responses. However, a detailed investigation of the relationship of eicosanoids in TB and TB-diabetes comorbidity (TB-DM) and association to disease pathology or bacterial burdens has not been studied. To study this, we examined the plasma levels of Lipoxin A4 (LXA4), 15-epi-LXA4, Leukotriene B4 (LTB4), and Prostaglandin E2 (PGE2) in individuals with either TB-DM, TB, diabetes mellitus (DM) or healthy controls (HC). Plasma levels of LXA4, 15-epi-LXA4, and PGE2 were significantly increased while the levels of LTB4 were significantly decreased in TB-DM and TB group compared to DM and HC. The ratio of LXA4 to LTB4 and 15-epiLXA4 to LTB4 was significantly enhanced in TB-DM compared to TB. Moreover, the levels of LXA4, 15-epi-LXA4 and the ratios of LXA4 to LTB4 and 15-epiLX4 to LTB4 were significantly increased in TB individuals with bilateral or cavitary disease and these markers also revealed a significant positive relationship with bacterial burden. At the completion of anti-tuberculosis therapy (ATT), levels of LXA4, 15-epi-LXA4, and PGE2 in TB-DM and TB groups were diminished and levels of LTB4 were enhanced in the TB group compared to pre-treatment. Our data imply that alteration and upregulation of eicosanoids are standard characteristics of TB-DM co-morbidity. Our data also demonstrate that modulation in the eicosanoid levels reflect disease severity and extent in TB and TB-DM and are modulated by ATT.