Role of fine needle aspiration cytology and cytohistopathological co-relation in thyroid lesions: experience at a tertiary care centre of North India

Background: Fine needle aspiration cytology is considered to be simple and cost effective technique for diagnosis of thyroid lesions. However the common limitations which may be encountered in FNAC may be associated with sampling error, dual pathology, cystic change or misinterpretation of morphology. The present study was therefore conducted to study the role of FNAC in diagnosis of thyroid lesions and to study the diagnostic pitfalls which may be encountered that limit the diagnosis of thyroid lesions.Methods: A retrospective study was conducted which included all the cases of thyroid lesions in which FNAC was done either directly or under image guidance over a period of five years. The cytomorphological diagnosis was correlated with histopathology to assess the diagnostic accuracy of FNAC in diagnosis of thyroid lesions.Results: Colloid goitre was the most common benign thyroid lesion while papillary carcinoma was the most common carcinoma constituting 50.2% and 5.2% of total cases. The maximum cyto-histopathological discordance was observed in cases of autoimmune thyroiditis (38%) and papillary carcinoma was most common lesion which was underdiagnosed on FNAC.Conclusions: The study concludes that although FNAC is safe, cost effective and sensitive technique for diagnosis of thyroid lesions but vigilant cyto-morphological interpretation in association with skilful aspiration and clinic-radiological co-relation is essential to avoid diagnostic pitfalls. This is even more important in cases showing focal neoplastic pathology or presence of dual pathology. Repeat image guided FNAC with clinical follow up is recommended in cases with strong clinical suspicion of malignancy

FLuID: Mitigating Stragglers in Federated Learning using Invariant Dropout

Federated Learning (FL) allows machine learning models to train locally on individual mobile devices, synchronizing model updates via a shared server. This approach safeguards user privacy; however, it also generates a heterogeneous training environment due to the varying performance capabilities across devices. As a result, straggler devices with lower performance often dictate the overall training time in FL. In this work, we aim to alleviate this performance bottleneck due to stragglers by dynamically balancing the training load across the system. We introduce Invariant Dropout, a method that extracts a sub-model based on the weight update threshold, thereby minimizing potential impacts on accuracy. Building on this dropout technique, we develop an adaptive training framework, Federated Learning using Invariant Dropout (FLuID). FLuID offers a lightweight sub-model extraction to regulate computational intensity, thereby reducing the load on straggler devices without affecting model quality. Our method leverages neuron updates from non-straggler devices to construct a tailored sub-model for each straggler based on client performance profiling. Furthermore, FLuID can dynamically adapt to changes in stragglers as runtime conditions shift. We evaluate FLuID using five real-world mobile clients. The evaluations show that Invariant Dropout maintains baseline model efficiency while alleviating the performance bottleneck of stragglers through a dynamic, runtime approach

Ad-Rec: Advanced Feature Interactions to Address Covariate-Shifts in Recommendation Networks

Recommendation models are vital in delivering personalized user experiences by leveraging the correlation between multiple input features. However, deep learning-based recommendation models often face challenges due to evolving user behaviour and item features, leading to covariate shifts. Effective cross-feature learning is crucial to handle data distribution drift and adapting to changing user behaviour. Traditional feature interaction techniques have limitations in achieving optimal performance in this context. This work introduces Ad-Rec, an advanced network that leverages feature interaction techniques to address covariate shifts. This helps eliminate irrelevant interactions in recommendation tasks. Ad-Rec leverages masked transformers to enable the learning of higher-order cross-features while mitigating the impact of data distribution drift. Our approach improves model quality, accelerates convergence, and reduces training time, as measured by the Area Under Curve (AUC) metric. We demonstrate the scalability of Ad-Rec and its ability to achieve superior model quality through comprehensive ablation studies

Interspecies Communication and Periodontal Disease

More than 500 bacterial strains may be found in dental plaque. In the beginning, the emphasis was laid on the isolation of bacteria in pure culture to define their properties. However, now, it has been well established that in nature the bacteria exist as a member of polymicrobial community or consortium of interacting species. Interactions among human oral bacteria are integral to the development and maturation of the plaque. These interactions occur at several levels including physical contact, metabolic exchange, small-signal molecule-mediated communication, and exchange of genetic material. This high level of interspecies interaction benefits the microorganism by providing a broader habitat range, effective metabolism, increasing the resistance to host defence, and enhancing their virulence. This generally has a detrimental effect on the host and is attributed to many chronic infections which poses a therapeutic challenge

Broad ligament pregnancy a diagnostic dilemma: a case report

Broad ligament pregnancy is one of the rarest forms of ectopic pregnancy with high risk of maternal mortality. Although ultrasonography is usually helpful in making the diagnosis but it is mostly established during laparotomy. 34 year old G2P1 with previous caesarean section reported at 8th month of pregnancy with inability to perceive foetal movements. Ultrasonography confirmed intrauterine fetal demise. Patient was taken for caesarean section after failed induction. Intraoperative diagnosis of broad ligament pregnancy was made and broad ligament along with fetus, sac, fallopian tube and ovary was excised. Post-operative period was uneventful

Orthodontic Treatment Considerations in Pregnancy: An Insight

Introduction: This article presents an insight on little known fact regarding orthodontic treatment in pregnancy and to find literature support in favor regarding orthodontic treatment during pregnancy. Literature was reviewed extensively to get results dental and orthodontic treatment during pregnancy. Discussion: Nowadays there are many adult patients seekingorthodontic treatment because of increase in awareness. In these adult patients, there are many pregnant females coming to orthodontist for treatment or a lady getting pregnant during the treatment. ‘Can a pregnant woman continue with orthodontic treatment or can she start with orthodontic treatment during pregnancy?’ This is a difficult question to answer but ‘Yes’, pregnant women can go for orthodontic treatment but with precautions. Present article gives us the information how to go about the treatment in pregnant women, the precautions to be taken, effect of drugs and hormonal changes on orthodontic treatment. Conclusion: Pregnant women can go for orthodontic treatment but with some precaution and some systemic and local condition limit the treatment modalities

AxBench: A Benchmark Suite for Approximate Computing Across the System Stack

Research areas: Approximate computing, Computer architectureAs the end of Dennard scaling looms, both the semiconductor industry and the research community are exploring for innovative solutions that allow energy efficiency and performance to continue to scale. Approximation computing has become one of the viable techniques to perpetuate the historical improvements in the computing landscape. As approximate computing attracts more attention in the community, having a general, diverse, and representative set of benchmarks to evaluate different approximation techniques becomes necessary. In this paper, we develop and introduce AxBench, a general, diverse and representative multi-framework set of benchmarks for CPUs, GPUs, and hardware design with the total number of 29 benchmarks. We judiciously select and develop each benchmark to cover a diverse set of domains such as machine learning, scientific computation, signal processing, image processing, robotics, and compression. AxBench comes with the necessary annotations to mark the approximable region of code and the application-specific quality metric to assess the output quality of each application. AxBenchwith these set of annotations facilitate the evaluation of different approximation techniques. To demonstrate its effectiveness, we evaluate three previously proposed approximation techniques using AxBench benchmarks: loop perforation [1] and neural processing units (NPUs) [2–4] on CPUs and GPUs, and Axilog [5] on dedicated hardware. We find that (1) NPUs offer higher performance and energy efficiency as compared to loop perforation on both CPUs and GPUs, (2) while NPUs provide considerable efficiency gains on CPUs, there still remains significant opportunity to be explored by other approximation techniques, (3) Unlike on CPUs, NPUs offer full benefits of approximate computations on GPUs, and (4) considerable opportunity remains to be explored by innovative approximate computation techniques at the hardware level after applying Axilog