Efficacy of Platelet-Rich Plasma in the Treatment of Diabetic Foot Ulcer.

Introduction Diabetic foot complications leading to limb amputations pose a global health concern. Platelet-rich plasma (PRP) gel has emerged as a promising method for ulcer healing, leveraging the growth factors provided by autologous PRP to enhance tissue healing. Therefore, we aimed to assess the frequency of the success of PRP therapy in the treatment of non-healing diabetic foot ulcers. Methods This quasi-experimental study, conducted in Lahore, Pakistan, from April 2021 to October 2022, enrolled 80 eligible individuals with non-responsive diabetic foot ulcers using a consecutive sampling technique. Inclusion criteria involved patients of both genders, aged 45-75 years, with unhealed diabetic foot ulcers, and exclusion criteria considered factors such as recurrent ulcers at the same site, smoking, and immunosuppressive or anticoagulant drug therapy. Baseline demographic details, ulcer measurements using a scale, and AutoCAD (Autodesk, Inc., San Francisco, California, United States)-assisted quantification of ulcer base were recorded. Autologous PRP injections were administered following strict aseptic protocols, with dressing changes and assessments performed at specified intervals over four weeks. Treatment success, defined as >90% healing after four weeks, was the primary outcome. Data analysis utilized IBM SPSS Statistics for Windows, Version 26.0 (Released 2019; IBM Corp., Armonk, New York, United States), employing post-stratification chi-square and t-tests where appropriate for significant differences. Results The mean age of the patients was 60.40 ± 9.72 years, the mean duration of diabetes was 9.48 ± 2.21 years, and the mean ulcer duration was 11.41 ± 1.63 months. The treatment success rate was 63.7%. Age, gender, and disease duration showed no significant impact on treatment success. However, patients with a normal BMI and shorter ulcer duration exhibited a significantly higher success rate (p <0.001 and p = 0.002, respectively). Conclusions This study reaffirms the efficacy of PRP in treating non-healing diabetic foot ulcers, aligning with previous research. Despite a slightly lower success rate compared to literature reports, PRP remains a promising agent for managing diabetic foot ulcers.

Testicular Mixed Germ Cell Tumor (TMGCT) Management: Addressing Infection and Tumor Marker Dynamics.

We report the case of a 23-year-old male presenting with right testicular swelling, post-coital pain, and fever. Initial MRI and local examination suggested testicular carcinoma. Elevated serum alpha-fetoprotein (AFP) and lactate dehydrogenase (LDH) levels were observed. Biopsy confirmed a mixed germ cell tumor (MGCT). Concurrently, the patient was diagnosed with an infection and treated with antibiotics. Remarkably, following antibiotic therapy, fever resolved, and tumor marker levels significantly decreased. Subsequent orchidectomy confirmed the diagnosis of MGCT. This case underscores the importance of recognizing and treating concurrent infections, which may influence both clinical presentation and tumor marker levels in testicular germ cell tumors.

Diagnostic Accuracy Of Contrast Enhanced Ct For Detection Of Renal Cell Carcinoma Taking Histopathology As Gold Standard.

BACKGROUND: Renal cell carcinoma being the commonest primary renal malignancy of adulthood accounts for approximately 80-90% renal malignant lesions. The purpose of radiological imaging modalities when devising the treatment options for renal masses is crucial as it significantly influence the clinical outcome and prognosis of the disease. Subjective impression by a radiologist for diagnosing a mass lesion is known to be critical and its precision is improved by contrast enhanced CT as demonstrated by certain retrospective analyses. We aimed to ascertain the diagnostic accuracy of contrast enhanced computed tomography to diagnose renal cell cancers by verifying through histopathology reported diagnoses. METHODS: This Cross-sectional (validation) study was carried out in Radiology and Urology departments of Ayub Teaching Hospital; Abbottabad, from 1st November 2020 to 30th April 2022. The study population included all admitted symptomatic patients with age range 18-70 years of either gender. The patients were subjected to detailed clinical examination and history and an ultrasound and contrast enhanced CT abdomen and pelvis. CT scans were reported under supervision of single consultant radiologist. Data was analysed in SPSS version 20.0. RESULTS: Mean age of the patients was 38.88±11.62 years ranging from 18-70 years and mean duration of symptoms was 54.64±49.171 ranging from 3-180 days. All of the total 113 patients underwent contrast enhanced CT scan and later operated to confirm the diagnoses by histopathology. The comparison yielded true positive (TP) cases to be 67, True Negative (TN) 16, False Positive (FP) 26, and 4 False Negative (FN) as per CT scan diagnoses. CT scan had a diagnostic Accuracy of 73.45% with 94.37% sensitivity and 38.10% specificity. CONCLUSIONS: Contrast-enhanced CT has a high sensitivity for making the diagnosis of renal cell carcinoma; however, its specificity is low. A multidisciplinary approach is necessary to overcome the low specificity. Therefore, collaboration between radiologists and urologic oncologists should be considered while devising treatment plan for patients.

POEMS (Polyneuropathy, Organomegaly, Endocrinopathy, Monoclonal Plasma Cell Disorder, and Skin Changes) Syndrome as a Sequela of Castleman Disease: A Case Report.

Polyneuropathy, organomegaly, endocrinopathy, monoclonal plasma cell disorder, and skin changes (POEMS) syndrome is a rare multisystemic paraneoplastic disorder caused by an underlying plasma cell dyscrasia. Its diagnosis is based on the presence of two mandatory criteria and at least one major and one minor criterion. We report a case of a 52-year-old female patient who presented with complaints of acrocyanosis, night sweats, scaly skin, and swelling on the left side of the neck. She was a known case of hypothyroidism, antiphospholipid syndrome, and cerebral venous thrombosis, and had other comorbidities as well. She also exhibited weakness and paresthesia of the limbs and muscle wasting in the hands. All necessary examinations and investigations were performed and the patient was eventually diagnosed with POEMS syndrome. She underwent chemotherapy along with immunotherapy initially, but as the disease relapsed, she was referred for high-dose therapy (HDT) and autologous stem cell transplantation.

The Role of Neutrophil-to-Lymphocyte Ratio in the Diagnosis of Acute Appendicitis.

BACKGROUND: Acute appendicitis (AA), a common reason for episodes of acute abdomen, is a surgical emergency. Its immediate diagnosis and management are of immense significance, as its diagnosis can become challenging at times, especially in resource-limited setups. The goal of this study was to ascertain the threshold value for the neutrophil-to-lymphocyte ratio (NLR) in diagnosing AA and to calculate the validity parameters for the NLR. METHODOLOGY:  A cross-sectional study was carried out involving 108 patients who were admitted to the surgical wards of Ayub Teaching Hospital, Abbottabad with suspicion of AA and subsequently underwent open appendectomy. Data was collected regarding the demography of the patients, physical examination findings, clinical presentations, and investigations including the histopathology and complete blood count, from which the NLR value was computed, and the Statistical Package for Social Sciences (SPSS), version 25.0 (IBM Corp., Armonk, NY) was utilized for the computation. Receiver operating characteristic (ROC) analysis was done to calculate the cut-off value of the NLR for diagnosing AA, and validity parameters were computed, taking into account statistical significance with a p-value < 0.05. RESULTS: Based on the ROC analysis, a threshold value for NLR indicating a positive appendectomy was determined to be 2.49 (sensitivity = 71.4% and 1-specificity = 12.5%) with an area under the curve of 90.6% (95% confidence interval {CI} 0.818-0.994, p<0.001). The sensitivity, specificity, and diagnostic accuracy of NLR for diagnosing AA were 71.43%, 87.5%, and 72.73%, respectively. CONCLUSION:  There is a strong correlation between NLR at a cut-off value of 2.49 and the diagnosis of AA. We suggest that NLR should be utilized as a complementary biomarker to clinical examination, aiding in the diagnosis of AA.

Oestrogen And Progesterone Receptors' Expression Pattern In Fibro-Adenoma Of The Female Breast.

BACKGROUND: Fibro-adenoma is the most common benign condition of the female breast comprising about 68% of all breast lumps. Fibroadenoma is an independent risk factor for the development of breast cancer. Complex fibroadenoma has a 2-3-fold increased risk ratio and simple fibroadenoma has 1.49 times increased risk ratio of developing cancer than the normal population over a period of 20 years. This study aimed to qualitatively check the frequency of oestrogen receptor-positive and progesterone receptor-positive cases of fibroadenoma in our region. METHODS: This cross-sectional study was conducted in the pathology department of Ayub Medical College, Abbottabad from June 2020 to December 2021. Biopsy confirmed cases of fibroadenoma were examined using immune-histochemical stains to score qualitatively the expression pattern of ER and PR. Data was analyzed and assessed using SPSS version 25. A p-value of ≤0.05 was considered statistically significant. RESULTS: The mean age of patients who presented with fibro-adenoma was 24.5±9.29 years with a median age of 21.5 years. In most cases, oestrogen receptor expression was mild 23 (54.76%) whereas progesterone receptor expression was severe 19 (45.23%). On chi-square test, the pattern of progesterone receptor expression for the category of hormone intake showed significant differences. Whereas, the pattern of oestrogen receptor expression for the categories of marital status, history of hormone intake, history of menstrual cycle and type of fibroadenoma showed no statistically significant difference. CONCLUSIONS: Further study into the pathogenesis of fibroadenoma is required to understand the role of ER and PR and explore the therapeutic potential of such drugs that affects these receptors. Cabling.

Effect Of Eight Hours Per Day Of Intermittent Self Prone Positioning For Seven Days On The Severity Of Covid-19 Pneumonia/ Acute Respiratory Distress Syndrome.

BACKGROUND: Prone positioning improves ventilation-perfusion mismatch, distribution of gravitational gradient in pleural pressure, and oxygen saturation significantly in patients with Covid pneumonia. We aimed to find out the efficacy of eight hours per day of intermittent selfprone positioning for seven days in patients affected with COVID-19 pneumonia/ ARDS. METHODS: This Randomized Clinical Trial was conducted in the Covid isolation wards of Ayub Teaching Hospital, Abbottabad. Patients suffering from COVID-19 pneumonia/ ARDS were enrolled with permuted block randomization into a control and an experimental group each consisting of 36 patients. Parameters of Pneumonia Severity Index (PSI) score along with other sociodemographic data was noted on a preformed structured questionnaire. Death was confirmed by requesting the death certificate of patients on the 90th day of enrolment. Data Analysis was done with SPSS Version 25. Tests of significance were applied to calculate the difference in the patients of the two groups with respect to respiratory physiology and survival. RESULTS: The mean age of the patients was 63.79±15.26 years. A total of 25 (32.9%) male and 47 (61.8%) female patients were enrolled. Statistically significant improvement was found in the respiratory physiology of the patients at 7th and 14th DOA between the groups. Pearson Chi-Square test of significance showed a difference in mortality between the two groups at 14th DOA (pvalue=0.011) but not at 90th DOA (p-value=0.478). Log Rank (Mantel-Cox) test of significance, applied on the Kaplan Meier curve and showed no statistically significant difference among the groups based on the survival of the patients. (p-value=0.349). CONCLUSIONS: Early transient improvement in respiratory physiology and mortality does occur with 8 hours of self-prone positioning for seven days but there is no effect on the 90-day survival of the patients. Thus, the impact of the manoeuvre on improving survival needs to be explored with studies having an application of the manoeuvre for a longer duration and period.

Small hydropower generation using pump as turbine; a smart solution for the development of Pakistan's energy.

Energy supply that is sustainable, effective, and economical has a strong association with socio-economic growth, particularly in developing countries such as Pakistan. Due to the ever-increasing gap between supply and demand, Pakistan has become an energy-deficient nation, with most people having no-to-limited access to power. Pakistan has been suffering from power shortages and an energy crisis because of its strong reliance on fossil-fuels to provide expensive electricity. Therefore, this paper offers a novel concept for developing Pakistan's energy by producing small-hydropower using Pump-As-Turbine (PAT), which is a form of Renewable-energy with lower environmental-impact and has not been used in Pakistan previously. PATs have shown several advantages over traditional hydro-turbines, such as minimum expenses, low-complexity, short delivery time, ease of spare parts, easy installation, availability in a large number of standard sizes, and massive production for broad-range of heads and flow rates. According to technical standards, any sort of pump could be used as PAT, including radial, mixed, single-stage, multi-stage etc. for power generation, which are capable of producing 5kW-1000kW of power, depending on their usage. However, Pakistan has shown little to no interest in exploring small/micro hydropower generation (PATs technology). Thus, this study offers public awareness and forward thinking regarding the use of advanced SHPs and draws the interests of legislators and different investors via solid recommendations about the cost-effective and environmental-friendly technology (PAT).

Diagnostic Accuracy Of Chest Radiograph In Interstitial Lung Disease As Confirmed By High Resolution Computed Tomography (HRCT) Chest.

Background: Interstitial Lung Disease (ILD) - an umbrella term encompassing about 100 different pathophysiological entities are usually defined as an irreversible, progressive fibrotic changes in the lung parenchyma that leads to difficult breathing and reduced gaseous exchange at the alveolar level. We aimed to quantify the validity of CXR for the diagnosis of ILD taking HRCT as gold standard in the population of Hazara division. Methods: This validation study was conducted during 11 June till 12 Dec 2019 in the radiology department of Ayub Teaching Hospital, Abbottabad on 60 adult patients aged 30-60 years who presented with progressive exertional dyspnoea. The patients were enrolled into the study via non probability, consecutive sampling technique. All the data was recorded on a self-developed structured questionnaire. Data was analyzed using SPSS version 20. Results: The mean age of study participants was 47.18±6.90 years SD with a range of 36-60years. The mean of time duration of symptoms was 9.66±1.7 years with a range of 7-12 years. There were 40 (66.7%) males and 20 (33.3%) females with a male to female ratio of 2:1. The sensitivity, specificity, PPV, NPV and Diagnostic Accuracy of CXR for the diagnosis of ILD as compared to HRCT was calculated to be 65.5%, 20%, 90%, 5% and 61.66% respectively. A chi square test of significance yielded a value of 0.51 for the diagnostic accuracy of CXR for ILD as compared to HRCT. Diagnostic ODDs ratio and Youden's Index yielded values of 47.37% and 0.145 respectively. All these parameters' points towards a lower utility of CXR for the diagnostic purpose in patients suspected with ILD. Conclusion: Chest x-ray is simple, non-invasive, economical and readily available alternative to HRCT but its specificity and diagnostic accuracy are questionable. CXR is a recommendable first line investigation for chest pathology workup but for a definitive diagnosis, one should not depend on CXR as it can miss the diagnosis.

Performance of cough monitoring by Albus Home, a contactless and automated system for nocturnal respiratory monitoring at home.

Introduction: Objective cough frequency is a key clinical end-point but existing wearable monitors are limited to 24-h recordings. Albus Home uses contactless motion, acoustic and environmental sensors to monitor multiple metrics, including respiratory rate and cough without encroaching on patient lifestyle. The aim of this study was to evaluate measurement characteristics of nocturnal cough monitoring by Albus Home compared to manual counts. Methods: Adults with respiratory conditions underwent overnight monitoring using Albus Home in their usual bedroom environments. Participants set-up the plug-and-play device themselves. For reference counts, each audio recording was counted by two annotators, and cough defined as explosive phases audio-visually labelled by both. In parallel, recordings were processed by a proprietary Albus system, comprising a deep-learning algorithm with a human screening step for verifying or excluding occasional events that mimic cough. Performance of the Albus system in detecting individual cough events and reporting hourly cough counts was compared against reference counts. Results: 30 nights from 10 subjects comprised 375 hours of recording. Mean±sd coughs per night were 90±76. Coughs per hour ranged from 0 to 129. Albus counts were accurate across hours with high and low cough frequencies, with median sensitivity, specificity, positive predictive value and negative predictive values of 94.8, 100.0, 99.1 and 100.0%, respectively. Agreement between Albus and reference was strong (intra-class correlation coefficient (ICC) 0.99; 95% CI 0.99-0.99; p<0.001) and equivalent to agreement between observers and reference counts (ICC 0.98 and 0.99, respectively). Conclusions: Albus Home provides a unique, contactless and accurate system for cough monitoring, enabling collection of high-quality and potentially clinically relevant longitudinal data.

Preparation and Evaluation of Polymer-Based Ultrasound Gel and Its Application in Ultrasonography.

Ultrasound imaging is a widely used technique in every health care center and hospital. Ultrasound gel is used as a coupling medium in all ultrasound procedures to replace air between the transducer and the patient's skin, as ultrasound waves have trouble in traveling through air. This research was performed to formulate an inexpensive alternative to commercially available ultrasound gel as it is expensive and imported from other countries. Different formulations with different concentrations of carbopol 980 (CAR 980) and methylparaben were prepared with natural ingredients such as aloe vera gel and certain available chemicals that have no harmful effects on the skin. To justify the efficiency of the formulations; necessary physicochemical characteristics such as visual clarity, homogeneity, transparency, skin irritation, antibacterial activity, pH, stability, spreadability, conductivity, acoustic impedance, viscosity, and cost were evaluated. Moreover, a comparison study was also conducted with commercially available ultrasound gel that was utilized as a control. All samples showed excellent transparency and no microbial growth. S1 was the only formulation that met all of the requirements for commercial ultrasound gel and produced images that were similar to those produced by commercial ultrasound gel. So, this formulation could be used as an alternative to expensive commercial ultrasound gel for taking images in hospitals and medical centers.

Comparison of parametric and non-parametric Bayesian inference for fusing sensory estimates in physiological time-series analysis.

The rapid proliferation of wearable devices for medical applications has necessitated the need for automated algorithms to provide labelling of physiological time-series data to identify abnormal morphology. However, such algorithms are less reliable than gold-standard human expert labels (where the latter are typically difficult and expensive to obtain), due to their large inter- and intra-subject variabilities. Actions taken in response to these algorithms can therefore result in sub-optimal patient care. In a typical scenario where only unevenly sampled continuous or numeric estimates are provided, without access to the "ground truth", it is challenging to choose which algorithms to trust and which to ignore, or even how to merge the outputs from multiple algorithms to form a more precise final estimate for individual patients. In this work, the novel application of two previously proposed parametric fully-Bayesian graphical models is demonstrated for fusing labels from (i) independent and (ii) potentially correlated algorithms, validated on two publicly available datasets for the task of respiratory rate (RR) estimation. These unsupervised models aggregate RR labels and estimate jointly the assumed bias and precision of each algorithm. Fusing estimates in this way may then be used to infer the underlying ground truth for individual patients. It is shown that modelling the latent correlations between algorithms improves the RR estimates, when compared to commonly employed strategies in the literature. Finally, it is demonstrated that the adoption of a strongly Bayesian approach to inference using Gibbs sampling results in improved estimation over the current state-of-the-art (e.g. hierarchical Gaussian processes) in physiological time-series modelling.

DeepMI: Deep multi-lead ECG fusion for identifying myocardial infarction and its occurrence-time.

Myocardial Infarction (MI) has the highest mortality of all cardiovascular diseases (CVDs). Detection of MI and information regarding its occurrence-time in particular, would enable timely interventions that may improve patient outcomes, thereby reducing the global rise in CVD deaths. Electrocardiogram (ECG) recordings are currently used to screen MI patients. However, manual inspection of ECGs is time-consuming and prone to subjective bias. Machine learning methods have been adopted for automated ECG diagnosis, but most approaches require extraction of ECG beats or consider leads independently of one another. We propose an end-to-end deep learning approach, DeepMI, to classify MI from Normal cases as well as identifying the time-occurrence of MI (defined as Acute, Recent and Old), using a collection of fusion strategies on 12 ECG leads at data-, feature-, and decision-level. In order to minimise computational overhead, we employ transfer learning using existing computer vision networks. Moreover, we use recurrent neural networks to encode the longitudinal information inherent in ECGs. We validated DeepMI on a dataset collected from 17,381 patients, in which over 323,000 samples were extracted per ECG lead. We were able to classify Normal cases as well as Acute, Recent and Old onset cases of MI, with AUROCs of 96.7%, 82.9%, 68.6% and 73.8%, respectively. We have demonstrated a multi-lead fusion approach to detect the presence and occurrence-time of MI. Our end-to-end framework provides flexibility for different levels of multi-lead ECG fusion and performs feature extraction via transfer learning.

Educational Environment In The Transition Phase Of Curriculum At Ayub Medical College Abbottabad, Pakistan.

BACKGROUND: Evaluation of the educational environment is key to the delivery of high-quality medical education. Especially, when an institute is in the transition phase of curriculum. In curriculum transformation phase of Ayub Medical College Abbottabad, no such evaluation has been done. This study aimed to find the direction of Educational environment in the transition phase curriculum of Ayub Medical College Abbottabad and compare different domains of educational environment with gender, residency, pre-medical education's medium of instruction, and doctors among sibling or parents. METHODS: This descriptive cross-sectional survey was conducted among students of integrated and traditional curriculum of Ayub Medical College, Abbottabad from 1st December 2019 to 29th February 2020. By Non-probability convenience sampling technique, pre-validated Dundee Ready Educational Environment Measure questionnaire was used. Descriptive and inferential statistics were calculated in SPSS v22. RESULTS: A total 149 (100%) participants, 66 (44.3%) males and 83 (55.7%) females with mean age of 20.5±1.07 years responded. Among total, 76 (51%) were from integrated curriculum and 73 (49%) were of traditional curriculum. Significant difference was found among different aspect of education environments and both classes. CONCLUSIONS: The current transitional phase of curriculum at Ayub Medical College Abbottabad is more positive than negative. Some areas like student social-self-perception still need improvement. Moreover, gender and place of birth affect student's perception about their learning environment.

Improving patient flow during infectious disease outbreaks using machine learning for real-time prediction of patient readiness for discharge.

BACKGROUND: Delays in patient flow and a shortage of hospital beds are commonplace in hospitals during periods of increased infection incidence, such as seasonal influenza and the COVID-19 pandemic. The objective of this study was to develop and evaluate the efficacy of machine learning methods at identifying and ranking the real-time readiness of individual patients for discharge, with the goal of improving patient flow within hospitals during periods of crisis. METHODS AND PERFORMANCE: Electronic Health Record data from Oxford University Hospitals was used to train independent models to classify and rank patients' real-time readiness for discharge within 24 hours, for patient subsets according to the nature of their admission (planned or emergency) and the number of days elapsed since their admission. A strategy for the use of the models' inference is proposed, by which the model makes predictions for all patients in hospital and ranks them in order of likelihood of discharge within the following 24 hours. The 20% of patients with the highest ranking are considered as candidates for discharge and would therefore expect to have a further screening by a clinician to confirm whether they are ready for discharge or not. Performance was evaluated in terms of positive predictive value (PPV), i.e., the proportion of these patients who would have been correctly deemed as 'ready for discharge' after having the second screening by a clinician. Performance was high for patients on their first day of admission (PPV = 0.96/0.94 for planned/emergency patients respectively) but dropped for patients further into a longer admission (PPV = 0.66/0.71 for planned/emergency patients still in hospital after 7 days). CONCLUSION: We demonstrate the efficacy of machine learning methods at making operationally focused, next-day discharge readiness predictions for all individual patients in hospital at any given moment and propose a strategy for their use within a decision-support tool during crisis periods.

A Spectral-longitudinal Model for Detection of Heart Attack from 12-lead Electrocardiogram Waveforms.

Cardiovascular diseases (CVDs) remain responsible for millions of deaths annually. Myocardial infarction (MI) is the most prevalent condition among CVDs. Although datadriven approaches have been applied to predict CVDs from ECG signals, comparatively little work has been done on the use of multiple-lead ECG traces and their efficient integration to diagnose CVDs. In this paper, we propose an end-to-end trainable and joint spectral-longitudinal model to predict heart attack using data-level fusion of multiple ECG leads. The spectral stage transforms the time-series waveforms to stacked spectrograms and encodes the frequency-time characteristics, whilst the longitudinal model helps to utilise the temporal dependency that exists in these waveforms using recurrent networks. We validate the proposed approach using a public MI dataset. Our results show that the proposed spectrallongitudinal model achieves the highest performance compared to the baseline methods.

Anaplastic thyroid carcinoma with leucocytosis.

BACKGROUND: Anaplastic thyroid carcinoma is a high-grade tumour with poor prognosis. Most of the cases are easily diagnosed on cytology and some of these are associated with increased neutrophils in cytology specimen as well as in the blood. The objective of the study is to determine the frequency of neutrophilia with fever in anaplastic thyroid carcinoma. METHODS: This descriptive cross-sectional study was performed in the Department of Pathology Ayub Teaching Hospital Abbottabad as well as in association with Advance lab Abbottabad. All the cases diagnosed as anaplastic thyroid carcinoma on cytology were included, histopathological examination was done only in 5 cases. The duration of study was from October 2016 to October 2019 were included in the study. RESULTS: Out of 150 cases of thyroid cytology 09 were diagnosed as anaplastic thyroid carcinoma. The mean age of patients was 65.7±6.96. Gender distribution was 5/9 (55.6%) males and 4/9 (44.4%) were females. Out of which 05 were confirmed on histopathology 3 patients died within a month and 1 patient refused a biopsy. All of these cases were associated with an increased number of neutrophils on cytology and WBC count is 04 cases showed leucocytosis. All of them presented with rapidly growing mass in long-standing goitre with a median duration of 2 months. Weight loss was seen in 4/9 (44.44%), 3/9 (33.33%) presented with hoarseness of voice while only 1/9 (11.1%) patient presented with superior vena caval syndrome. CONCLUSION: In long-standing goitre rapid increase in size with fever and leucocytosis are suggestive of anaplastic thyroid carcinoma which should be investigated promptly.

Multi-Modal Diagnosis of Infectious Diseases in the Developing World.

In low and middle income countries, infectious diseases continue to have a significant impact, particularly amongst the poorest in society. Tetanus and hand foot and mouth disease (HFMD) are two such diseases and, in both, death is associated with autonomic nervous system dysfunction (ANSD). Currently, photoplethysmogram or electrocardiogram monitoring is used to detect deterioration in these patients, however expensive clinical monitors are often required. In this study, we employ low-cost and mobile wearable devices to collect patient vital signs unobtrusively; and we develop machine learning algorithms for automatic and rapid triage of patients that provide efficient use of clinical resources. Existing methods are mainly dependent on the prior detection of clinical features with limited exploitation of multi-modal physiological data. Moreover, the latest developments in deep learning (e.g. cross-domain transfer learning) have not been sufficiently applied for infectious disease diagnosis. In this paper, we present a fusion of multi-modal physiological data to predict the severity of ANSD with a hierarchy of resource-aware decision making. First, an on-site triage process is performed using a simple classifier. Second, personalised longitudinal modelling is employed that takes the previous states of the patient into consideration. We have also employed a spectrogram representation of the physiological waveforms to exploit existing networks for cross-domain transfer learning, which avoids the laborious and data intensive process of training a network from scratch. Results show that the proposed framework has promising potential in supporting severity grading of infectious diseases in low-resources settings, such as in the developing world.

Severity detection tool for patients with infectious disease.

Hand foot and mouth disease (HFMD) and tetanus are serious infectious diseases in low- and middle-income countries. Tetanus, in particular, has a high mortality rate and its treatment is resource-demanding. Furthermore, HFMD often affects a large number of infants and young children. As a result, its treatment consumes enormous healthcare resources, especially when outbreaks occur. Autonomic nervous system dysfunction (ANSD) is the main cause of death for both HFMD and tetanus patients. However, early detection of ANSD is a difficult and challenging problem. The authors aim to provide a proof-of-principle to detect the ANSD level automatically by applying machine learning techniques to physiological patient data, such as electrocardiogram waveforms, which can be collected using low-cost wearable sensors. Efficient features are extracted that encode variations in the waveforms in the time and frequency domains. The proposed approach is validated on multiple datasets of HFMD and tetanus patients in Vietnam. Results show that encouraging performance is achieved. Moreover, the proposed features are simple, more generalisable and outperformed the standard heart rate variability analysis. The proposed approach would facilitate both the diagnosis and treatment of infectious diseases in low- and middle-income countries, and thereby improve patient care.