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Sickle Cell Anemia (SCA) is a disorder in Red Blood Cells (RBCs) of human blood. Children under five years and pregnant women are mostly affected by SCA. Early diagnosis of this ailment can save lives. In recent years, the computer aided diagnosis of SCA is preferred to resolve this issue. A novel and effective deep learning approach for identification of sickle cell anemia is proposed in this work. Around nine hundred microscopic images of human red blood cells are obtained from the public database ‘erythrocytes IDB’. All the images are resized uniformly. About 2048 deep features are extracted from the fully connected layer of pre-trained model InceptionV3. These features are further subjected to classification using optimization-based methods. An improved wrapper-based feature selection technique is implemented using Multi- Objective Binary Grey Wolf Optimization (MO-BGWO) approach with KNN and SVM for classification. The detection of sickle cell is also performed using typical InceptionV3 model by using SoftMax layer. It is observed that the performance of the proposed system seems to be high when compared to the classification using the original InceptionV3 model. The results are validated by various evaluation metrics such as accuracy, precision, sensitivity, specificity and F1-score. The SVM classifier yields high accuracy of about 96%. The optimal subset of deep features along with SVM enhances the system performance in the proposed work. Thus, the proposed approach is appropriate for pathologists to take early clinical decisions on detection of sickle cells.

Background Men who have sex with men (MSM) in low-to-middle-income countries bear a high burden of HIV and a significant proportion marry women to satisfy socio-cultural norms. It has previously been demonstrated that HIV prevalence is higher among married versus unmarried MSM, but limited data are available on HIV prevalence among their wives. Methods We recruited 149 married MSM couples where the husband disclosed his MSM behavior to his wife (disclosed MSM) and 150 married MSM who had not disclosed their MSM behavior to their wives (undisclosed MSM), in three Indian cities. All participants completed interviewer-administered surveys and underwent HIV testing. Multivariable logistic regression was used to assess correlates of HIV among wives of MSM. Descriptive statistics were used to compare characteristics of disclosed and undisclosed MSM. Results HIV prevalence in disclosed MSM, their wives, and undisclosed MSM was 46.9%, 27.9%, and 34.9%, respectively. In multivariable analysis, the strongest predictor of HIV prevalence among wives of MSM was husband’s HIV positive status (adjusted odds ratio: 13.4; 95% confidence interval: 5.44–33.2). Both disclosed and undisclosed MSM reported high levels of risk behavior including infrequent condom use. Most undisclosed MSM reported that they did not intend to disclose MSM behavior due to stigma. Conclusions We observed an extremely high HIV prevalence among women married to MSM despite low levels of individual risk behavior. Interventions are urgently needed in this vulnerable population particularly given the challenges related to disclosure of same sex behavior among their husbands.

Uyu’ atau lagu mendodoi anak merupakan salah satu jenis tradisi lisan rakyat bagi masyarakat etnik Lundayeh. Isi kandungan uyu’ penuh dengan unsur nasihat, lambang sosiobudaya, nilai kepahlawanan dan falsafah. Namun, uyu’ mula dilupakan khasnya dalam kalangan generasi muda. Oleh itu, kajian ini dilakukan untuk menghidupkan semula tradisi lisan dengan membuat penelitian kepada kandunganya. Untuk kajian ini, pengkaji akan memfokuskan kepada kosa kata arkaik golongan kata kerja dari uyu’ naso anak di Long Pasia. Pengkaji telah menggunakan pendekatan dengan kajian kualitatif melalui kaedah kajian lapangan dan temu bual bagi menghasilkan dokumentasi kosa kata arkaik golongan kata kerja dari uyu’ naso anak melalui kajian kepada tiga generasi (datuk nenek, ibu bapa dan kanak-kanak). Seterusnya, data akan dianalisis menggunakan skala keterancaman bahasa UNESCO (2009), edisi yang menetapkan tahap bahaya sedikit berbeza daripada edisi sebelumnya. Terminologi baharu ini adalah berdasarkan rangka kerja Vitality dan Endangerment Bahasa UNESCO yang menetapkan enam darjah daya hidup atau bahaya berdasarkan sembilan faktor. Dapatan kajian mendapati bahawa generasi kanak-kanak kehilangan kosa kata paling tinggi, apabila menyatakan 10 daripada 27 kosa kata kerja adalah arkaik. Hal ini menunjukkan bahawa kanak-kanak sudah tidak menuturkan bahasa warisan di rumah. Seharusnya, uyu’ etnik Lundayeh mesti dipergiatkan kerana mengandungi pelbagai unsur bahasa, selain melambangkan sosiobudaya, falsafah dan nilai masyarakat. Langkah mengaplikasikannya adalah salah satu usaha untuk memperkasa dan melestarikan kosa kata bahasa yang hampir pupus agar terus diwarisi generasi ke generasi. “Uyu” or song lullaby is a type of folk oral tradition in theLundayeh ethnic community. The content of “uyu” is full of elements of advice, socio-cultural symbols, heroic values, and philosophy. However, “uyu” is gradually being forgotten, especially among the younger generation. Therefore, this study was conducted to revive the “uyu’ oral tradition by examining its content. In this study, the researchers focused on the archaic vocabulary of the verbs from “uyu’ naso anak” in Long Pasia. The researchers adopted a qualitative research using field research methods and interviews to produce archaic vocabulary documentation of verbs from “uyu’ naso anak” through a study of three generations (grandparents, parents and children). Next, the data were analyzed using the UNESCO language risk scale (2009), the edition of which sets the level of danger slightly different than the previous edition. This new phrasing depends on the UNESCO Language Vitality and Endangerment framework that sets six degrees of vitality or danger based on nine factors. The findings of the study found that the children (younger generation) has lost the highest vocabulary, when stating 10 out of 27 vocabularies were archaic. Thus, this indicates that the children no longer speak the heritage language at home. The Lundayeh ethnic “uyu” must be promoted because it contains various elements of language other than community values. One of the efforts to strengthen and preserve the vocabulary of a language that is almost extinct is through application to ensure the language continues to be inherited from generation to generation.

Background Children and adolescents living with HIV (CALHIV) face unique challenges, including poorer treatment outcomes, risk for drug-resistance mutations (HIVDRMs), and limited drug formulations. We estimated viral suppression (VS) prevalence and evaluated predictors of VS and HIVDRMs in Kenya. Methods From 2018–2020, CALHIV 1–19 years on antiretroviral therapy (ART) >6 months were enrolled in this cross-sectional study. Participants underwent viral load (VL) testing; those with VL ≥1000 copies/mL had HIVDRM testing. Sociodemographic questionnaires and medical record abstraction were completed. VS prevalence (VL Results Nine hundred and sixty-nine participants were enrolled. VS prevalence was .80 (95% CI: .78–.83). Being on ART >24 months (adjusted PR [aPR]: 1.22; 95% CI: 1.06–1.41), an integrase strand transfer inhibitor–containing regimen (1.13; 1.02–1.26), and attending a level 3 health facility (1.23; 1.11–1.36) were associated with VS. Missing ≥3 doses of ART in the past month (aPR: .73; 95% CI: .58–.92), having a viremic mother with HIV (.72; .53–.98), and having 3–7 (.90; .83–.97), 8–13 (.89; .82–.97), or ≥14 (.84; .77–.92) compared with Conclusions HIV treatment programs should target interventions for pediatric patients at risk for treatment failure—namely, those with a caregiver with failed VS and those struggling with adherence.

The diagnostic and clinical overlap of early mild cognitive impairment (EMCI), mild cognitive impairment (MCI), late mild cognitive impairment (LMCI) and Alzheimer disease (AD) is a vital oncological issue in dementia disorder. This study is designed to examine Whole brain (WB), grey matter (GM) and Hippocampus (HC) morphological variation and identify the prominent biomarkers in MR brain images of demented subjects to understand the severity progression. Curve evolution based on shape constraint is carried out to segment the complex brain structure such as HC and GM. Pre-trained models are used to observe the severity variation in these regions. This work is evaluated on ADNI database. The outcome of the proposed work shows that curve evolution method could segment HC and GM regions with better correlation. Pre-trained models are able to show significant severity difference among WB, GM and HC regions for the considered classes. Further, prominent variation is observed between AD vs. EMCI, AD vs. MCI and AD vs. LMCI in the whole brain, GM and HC. It is concluded that AlexNet model for HC region result in better classification for AD vs. EMCI, AD vs. MCI and AD vs. LMCI with an accuracy of 93, 78.3 and 91% respectively.

The systolic and diastolic heart failure (HF) subjects are typically categorized based on clinical indices only. The relationship between different stages of systolic and diastolic heart failure and left ventricle (LV) myocardial tissue variations is presented in this work. The corr-entropy and optimized edge criterion has been incorporated into the level set (CEOELS) for effective segmentation of myocardium in cardiovascular magnetic resonance images to handle noise, intensity inhomogeneity and contour initialization. In order to learn shape and local variations in segmented myocardium, Krawtchouk moment features are computed for ten different moment orders. The relevant extracted features are obtained through Harris hawks optimization algorithm. The optimized features are fed to support vector machine (SVM) that uses fivefold cross-validation approach for classification. Experimental results show that CEOELS has provided better segmentation of LV blood cavity and myocardium with a similarity measure of 0.93 and 0.92, respectively. It is also observed that individual Krawtchouk moment orders greater than 30 have provided better HF prediction performance. Consequently, optimized Krawtchouk moment features produced an increased overall accuracy (80.8%) than individual feature sets. Significant improvement has also been achieved in distinction of hyperdynamic patients from normal and systolic dysfunction subjects that is less explored.

BackgroundThe World Health Organization early warning indicators (EWIs) permit surveillance of factors associated with the emergence of HIV drug resistance (HIVDR). We examined cross- and within-region performance on HIVDR EWIs for selected HIV care and treatment clinics (CTCs) in five regions of southern Tanzania.MethodsIn 2016, we retrospectively collected EWI data from 50 CTCs for the January to December 2013 period. EWIs included the following: on time ART pick-up, retention on ART, ARV stockouts, and pharmacy prescribing and dispensing practices. Data for pediatric and adult people living with HIV were abstracted from source files, and frequencies and proportions were calculated for each EWI overall, as well as stratified by region, facility, and age group.ResultsAcross and within all regions, on average, on-time pick-up of pills (63.0%), retention on ART (76.0%), and pharmacy stockouts (69.0%) were consistently poor for the pediatric population. Similarly, on-time pill pick up (66.0%), retention on ART (72.0%) and pharmacy stockouts (53.0%) for adults were also poor. By contrast, performance on pharmacy prescribing and dispensing practices were as desired for both pediatric and adult populations with few facility-level exceptions.ConclusionIn this study, regions and facilities in the southern highlands of Tanzania reported widespread presence of HIVDR risk factors, including sub-optimal timeliness of pill pickup, retention on ART, and drug stockouts. Routine EWI monitoring provides a check on quality of service delivery, as well as affordable, large-scale surveillance for HIVDR risk factors. EWI monitoring remains relevant particularly in the context of new ART drug roll-out such as dolutegravir, during the COVID-19 pandemic when resultant HIV service disruptions require careful monitoring, and for virologic suppression as countries move closer to epidemic control.

In this study, an attempt has been made to differentiate HEp-2 cellular shapes using Bag-of-keypoint features and optimization. For this, the images are considered from a publicly available database. To increase the cell structure visibility, the images are pre-processed using edge-sensitive local contrast enhancement. Further, the Speeded-up Robust Feature (SURF) keypoints are extracted and Bag-of-keypoints for each shape are generated. These features are subjected to Ant Colony Optimization (ACO) algorithm for feature selection. The optimal features obtained are then fed to Support Vector Machine (SVM) and k-Nearest Neighbour (kNN) classifiers. Results show that the ACO algorithm can identify the optimal features that characterize the cellular shapes. SVM and kNN are able to differentiate between the shapes with an average classification accuracy of 93.6% and 94.8% respectively. Since differential diagnosis of HEp-2 cellular shapes is significant in the disease-specific prognosis and treatment, this study seems to be clinically relevant.

Background Cardiovascular and metabolic regulation is governed by neurohumoral signalling in relevant organs such as kidney, liver, pancreas, duodenum, adipose tissue, and skeletal muscle. Combined targeting of relevant neural outflows may provide a unique therapeutic opportunity for cardiometabolic disease. Objectives We aimed to investigate the feasibility, safety, and performance of a novel device-based approach for multi-organ denervation in a swine model over 30 and 90 days of follow-up. Methods Five Yorkshire cross pigs underwent combined percutaneous denervation in the renal arteries and the common hepatic artery (CHA) with the iRF Denervation System. Control animals (n = 3) were also studied. Specific energy doses were administered in the renal arteries and CHA. Blood was collected at 30 and 90 days. All animals had a pre-terminal procedure angiography. Tissue samples were collected for norepinephrine (NEPI) bioanalysis. Histopathological evaluation of collateral structures and tissues near the treatment sites was performed to assess treatment safety. Results All animals entered and exited the study in good health. No stenosis or vessel abnormalities were present. No significant changes in serum chemistry occurred. NEPI concentrations were significantly reduced in the liver (− 88%, p = 0.005), kidneys (− 78%, p p = 0.018) and duodenum (− 95%, p = 0.028) following multi-organ denervation treatment compared to control animals. Histologic findings were consistent with favourable tissue responses at 90 days follow-up. Conclusions Significant and sustained denervation of the treated organs was achieved at 90 days without major safety events. Our findings demonstrate the feasibility of multi-organ denervation using a novel iRF Denervation System in a single procedure.

The Walter Reed Army Institute of Research (WRAIR) supports more than 350,000 people on lifesaving HIV treatment in Kenya, Nigeria, Tanzania, and Uganda through funding from the U.S. President’s Emergency Plan for AIDS Relief (PEPFAR). Here, we review and synthesize the range of impacts WRAIR’s implementation science portfolio has had on PEPFAR service delivery for military and civilian populations since 2003. We also explore how investments in implementation science create institutional synergies within the U.S. Department of Defense, contributing to broad global health engagements and improving health outcomes for populations served. Finally, we discuss WRAIR’s contributions to PEPFAR priorities through use of data to drive and improve programming in real time in the era of HIV epidemic control and public health messaging that includes prevention, the 95-95-95 goals, and comorbidities.

In this paper we focus on the Lundayeh indigenous minority in Long Pasia, Sabah by examining how their traditional food practices help them navigate environmental challenges. Deforestation and logging threaten the very core of the Lundayeh identity because the community’s livelihood as subsistence farmers depends on hunting as well as gathering forest resources. This paper argues that, despite the continuous challenges, Lundayeh food practices, albeit exercised in modified forms, provide an avenue to revisit past traditions in order for the community’s indigeneity and sovereignty to survive and be safeguarded. The findings of this research project suggest that through hunting techniques, foraging, paddy cultivation, agricultural cooperative work, as well as religiously sensitive food adaptation practices, the Lundayeh’s relationship with the land endures, which in turn, secures the community’s indigenous identity.

This article examines “death” in a funeral chant set in the plantation estates of Malaysia, and written in English and Tamil, as a metaphor for the birth of the nation. It explores how the death of communal linguistic elements, both in orality and symbolic references, lead to the deconstruction of motherland identity markers which are then replaced by the reconstruction of diasporic identities that are observable through the use of standardized English. For this purpose, the Malaysian Indian life-writer, Muthammal Palanisamy’s English version of an oppari (Tamil for funeral chant), which was published in Malaysia (2002) will be read in relation to the Tamil version published in India (2007) through transliterated and translated texts of the chant. In so doing, the paper highlights the inherent gap between the two versions that can be usefully deployed to address whether English is an enabling tool through which ethnic Indians can express their identities in a postcolonial nation like Malaysia or is it perpetually contaminated by colonial history and values. On the other hand, the paper also draws attention to the question of whether the displacement of the vernacular language, i.e., Tamil, witnesses the inevitable cultural death of a diasporic community or does it display a form of inclusivity within the polyglot linguistic environment of the adopted land, Malaysia.

This paper explores the power of stock trading trend using an integrated New Threshold Fuzzy Cognitive Maps (NTFCMs) Markov chain model. This new model captures the positive as well as the negative jumps and predicts the trend for different stocks over 4 months which follow an uptrend, downtrend and a mixed trend. The mean absolute per cent error (MAPE) tolerance limits, the root mean square error (RMSE) tolerance limits are determined for various stock indices over a multi-timeframe period and observed for the existing methods lying within the defined limits. The results show for every ‘n’ number of predictions made, the predicted close value of the day’s stock price was within tolerance limit with 0 % error and with 100% accuracy in predicting the future trend.

This article investigates the representation of nature in contemporary Malaysian life-writings in English by female writers of Malay, Chinese and Indian ethnic origins. Adibah Amin’s This End of th...

Prognosis of in a dementia disorder is a tedious task in preclinical stage. Ventricle pathology changes in dementia appear to be overlapped for neuro degeneration in brain. Identification of these overlaps among the groups severity helps to understand the pathogenesis of this disorder. In this work impact of changes in ventricle region on severity stages of dementia is observed using dual deep learning techniques (DDLT). Alzheimer's Disease Neuroimaging Initiative (ADNI) database that contains 1169 MR images are used in this study. Segmentation of ventricle region is carried out using multilevel threshold based Grey Wolf Optimization (GWO) technique. The feature vectors obtained from combined AlexNet and ResNet are analysed. The fused feature vectors are given to support vector machine (SVM) to observe the severity changes. Consequently, symmetry analysis of ventricle is carried out to perceive the distinctive changes in progression. The obtained results show that ventricle region is accurately delineated from other region with optimized thresholds. The segmented ventricle shows better correlation for all considered classes (> 0.9). It is observed that DDLT with multiclass SVM provides an improved accuracy of about 79.87% compared to individual transfer learning such as AlexNet (74%) and ResNet (76.53%). Further, symmetry analysis shows that left side ventricle with DDLT features shows an improved performance than right side for onset stages. Further, clinical correlation of left ventricle seems to be statically significant (p