Exploiting nuclear-mitochondrial cross-talk in theranostics: Enhancing drug delivery and diagnostic precision.

The dynamic interplay between nuclear and mitochondrial processes plays a pivotal role in cellular homeostasis and disease progression. Exploiting this nuclear-mitochondrial cross-talk has emerged as a promising avenue in the field of theranostics, offering enhanced drug delivery and diagnostic precision for a wide range of medical conditions, particularly cancer. This abstract provides a brief overview of the key concepts and recent advancements in this rapidly evolving field. Recent research has elucidated the significance of mitochondrial dysfunction in various diseases, including cancer. Mitochondria, often referred to as the "powerhouses" of the cell, not only regulate energy production but also contribute to critical processes such as apoptosis, ROS generation, and metabolic signaling. Dysregulation of these mitochondrial functions is frequently associated with disease pathogenesis. In theranostics, the targeted modulation of mitochondrial function holds great promise. Mitochondria-targeted drug delivery systems have been designed to selectively deliver therapeutic agents to these organelles, thereby mitigating mitochondrial dysfunction while minimizing off-target effects. This precise drug delivery enhances the therapeutic efficacy of anticancer drugs and reduces the risk of drug resistance. Moreover, the diagnostic potential of nuclear-mitochondrial cross-talk is being harnessed to develop novel biomarkers and imaging techniques. Mitochondrial DNA mutations and alterations in mitochondrial metabolism serve as valuable indicators of disease progression and drug responsiveness. Non-invasive imaging modalities, such as positron emission tomography (PET) and magnetic resonance imaging (MRI), have been employed to visualize mitochondrial activity and assess therapeutic outcomes.

Diagnostic precision of Truenat® technique and co-relation of ALT levels with HBV-DNA viral load among HBsAg positive patients at a tertiary care hospital in Eastern Uttar Pradesh.

Background and Objectives: In India, it is estimated that there are 40 million people suffering from Hepatitis B virus (HBV). Quantification of the viral burden is an important laboratory tool in the management. However, widespread use of different HBV-DNA assays is still affected by the high cost and variable diagnostic precision. The present study was conducted to evaluate the diagnostic precision and co-relation of ALT levels with HBV-DNA by Truenat®-PCR. Materials and Methods: In this prospective cross-sectional study a total of 567 serums were collected from patients by rapid HBsAg, and processed for liver function tests (LFT). The viral HBV-DNA amplification detection was carried out through by Truenat®-PCR test. Results: Out of 567 samples, 452 samples were found to be positive by both rapid and Truenat®-PCR and 106 were negative for HBV-DNA followed by 9 invalid. High ALT level found in 73% of positive patients who had HBV-DNA level (>100000 copies/ml) which is significantly higher in 447 patients as compared to those have below ≤100000 copies/ml. Conclusion: Truenat®-PCR technique is a highly sensitive and can be performed with low resources for effective control of HBV infection. Evaluation of HBV-DNA levels and serum ALT levels showed a significant proportion of patient harbored ongoing viral replication and disease progression.

Anti-neurofascin-155 antibody mediated a distinct phenotype of chronic inflammatory demyelinating polyradiculoneuropathy.

BACKGROUND: To investigate Ranvier's autoantibodies prevalence and isotypes in various peripheral neuropathy variants, compare clinical features between seronegative and seropositive patients, and elucidate immune mechanisms underlying antibody generation. METHODS: Antibodies against anti-neurofascin-155 (NF155), NF186, contactin-1 (CNTN1), CNTN2, contactin-associated protein 1 (CASPR1), and CASPR2 were identified through cell-based assays. Plasma cytokines were analyzed in anti-NF155 antibody-positive chronic inflammatory demyelinating polyneuropathy (NF155+ CIDP) and Ranvier's antibodies-negative CIDP (Ab- CIDP) patients using a multiplexed fluorescent immunoassay, validated in vitro in a cell culture model. RESULTS: In 368 plasma samples, 50 Ranvier's autoantibodies were found in 45 individuals, primarily in CIDP cases (25 out of 69 patients) and in 10 out of 122 Guillain-Barré syndrome patients. Anti-NF155 and CNTN1-IgG were exclusive to CIDP. Fourteen samples were NF155-IgG, primarily IgG4 subclass, linked to CIDP features including early onset, tremor, sensory disturbance, elevated CSF protein, prolonged motor latency, conduction block, and poor treatment response. NF155-IgG had low sensitivity (20.28%) but high specificity (100%) for CIDP, rising to 88.88% with tremor and prolonged motor latency. Cytokine profiling in NF155+ CIDP revealed distinct immune responses involving helper T cells, toll-like receptor pathways. Some NF155+ CIDP patients had circulating NF155-specific B cells producing NF155-IgG without antigen presence, suggesting therapeutic potential. CONCLUSION: The study emphasizes the high specificity and sensitivity of NF155-IgG for diagnosing CIDP characterized by distinctive features. Further investigation into circulating NF155-specific B cell phenotypes may pave the way for B cell directed therapy.

Beyond the Scope: Advancing Otolaryngology With Artificial Intelligence Integration.

The integration of artificial intelligence (AI) into otolaryngology heralds a new era of enhanced diagnostic precision, improved treatment strategies, and better patient outcomes. This advancement, however, brings to the fore the essential role of education and training in maximizing AI's potential within the field. The diverse spectrum of otolaryngology, encompassing audiology, rhinology, and sleep medicine, presents numerous opportunities for AI applications from predicting hearing loss progression and optimizing cochlear implant settings to managing chronic sinusitis and predicting the success of treatments for obstructive sleep apnea. Such innovations necessitate a paradigm shift in educational frameworks, merging traditional clinical skills with AI literacy. This involves introducing AI concepts, tools, and applications specific to otolaryngology in the curriculum, ensuring practitioners are equipped to leverage AI for diagnostics, patient monitoring, and surgical planning. Exploring the potential of large language models (LLMs) in medical education, simulating clinical scenarios for risk-free diagnostic practice and decision-making, is imperative. Underscoring the importance of continuous education for established otolaryngologists through workshops and seminars on the latest AI tools is another essential goal. Moreover, highlighting the need for a collaborative approach to address ethical considerations and ensure the responsible integration of AI while advocating for a multidisciplinary educational strategy is an important asset. As we navigate this transition, the commitment to training and education becomes paramount, preparing the otolaryngology community to embrace AI-driven healthcare innovations.

Artificial Intelligence in Endodontics: A Scoping Review.

Artificial intelligence (AI) is transforming the diagnostic methods and treatment approaches in the constantly evolving field of endodontics. The current review discusses the recent advancements in AI; with a specific focus on convolutional and artificial neural networks. Apparently, AI models have proved to be highly beneficial in the analysis of root canal anatomy, detecting periapical lesions in early stages as well as providing accurate working-length determination. Moreover, they seem to be effective in predicting the treatment success next to identifying various conditions e.g., dental caries, pulpal inflammation, vertical root fractures, and expression of second opinions for non-surgical root canal treatments. Furthermore, AI has demonstrated an exceptional ability to recognize landmarks and lesions in cone-beam computed tomography scans with consistently high precision rates. While AI has significantly promoted the accuracy and efficiency of endodontic procedures, it is of high importance to continue validating the reliability and practicality of AI for possible widespread integration into daily clinical practice. Additionally, ethical considerations related to patient privacy, data security, and potential bias should be carefully examined to ensure the ethical and responsible implementation of AI in endodontics.

Diagnostic Precision in Lyme borreliosis: Assessing VlsE and C6 Antigens in a Pediatric Cohort.

(1) Background: Lyme borreliosis (LB) is a tick-borne disease known for its diagnostic challenges. Conventional two-tiered testing (CTTT) for antibodies is time-consuming, has low sensitivity in the early stages of disease, and sometimes generates false-positive IgM immunoblots. To tackle this issue, modified two-tiered testing (MTTT) was introduced, incorporating recombinant VlsE and C6 antigens to enhance diagnostic accuracy. (2) Methods: In this prospective study, we enrolled children exhibiting symptoms indicative of LB. We collected serum samples at various intervals and subjected them to analysis using standard enzyme immunoassays. We then compared these results with the outcomes from the VlsE and C6 assays. (3) Results: In our study, all 33 patients displaying erythema migrans (EM), a characteristic symptom of LB, exhibited positive responses to the C6 antigen. This finding underscores the potential utility of the C6 antigen as a reliable diagnostic tool for LB. Additionally, we observed a significant reduction in anti-VlsE antibody levels following antibiotic treatment in EM patients. (4) Conclusions: The utilization of recombinant VlsE and C6 antigens in LB diagnostics and monitoring has yielded promising results. Nonetheless, it is imperative for clinicians to exercise caution and interpret results in conjunction with clinical findings, considering the dynamic nature of medical guidelines. Even with recombinant antigen tests, some children with EM tested negative, highlighting the importance of clinical diagnosis for treatment decisions. Furthermore, clinicians should be mindful of the possibility of persistently positive VlsE/C6 test results during LB treatment monitoring.

The utility of histology in assessing the cause of death in medico-legal autopsies in selected trauma deaths: Suspension-, immersion-, fire-, and traffic-related.

The goal of a medico-legal autopsy is primarily to determine the cause and manner of death. To this end, the pathologist often uses auxiliary analyses, including histology. However, the utility of routine histology in all medico-legal autopsies is unknown. Earlier studies on the utility of routine histology have shown inconsistent effects, with some studies recommending it and others rejecting it. To study the degree to which histology informs on the underlying cause of death, we sent autopsy reports from suspension-, immersion-, fire-, and traffic-related deaths to senior board-certified forensic pathologists and had them assess the cause of death, first without knowledge of the histological findings and then with knowledge thereof. Fifty cases were identified in each of four subgroups: fire-, immersion-, suspension-, and traffic-related deaths. The autopsy reports were anonymized, and the histological findings and conclusions were removed. Two board-certified forensic pathologists independently reviewed the reports in each subgroup and assessed the manner and underlying cause of death (including their certainty of this assessment on a five-level scale) with and without access to histological findings. The probability of changing the underlying cause of death posthistology was low in all study groups. There was a slight increase in the degree of certainty posthistology in cases where the underlying cause of death was not changed, but only when the antehistology certainty was low. Our results suggest that histology does not meaningfully inform on the underlying cause of death in suspension-, immersion-, fire-, and traffic-related deaths except when antehistology certainty is low.

Oral squamous cell carcinoma detection using EfficientNet on histopathological images.

Introduction: Oral Squamous Cell Carcinoma (OSCC) poses a significant challenge in oncology due to the absence of precise diagnostic tools, leading to delays in identifying the condition. Current diagnostic methods for OSCC have limitations in accuracy and efficiency, highlighting the need for more reliable approaches. This study aims to explore the discriminative potential of histopathological images of oral epithelium and OSCC. By utilizing a database containing 1224 images from 230 patients, captured at varying magnifications and publicly available, a customized deep learning model based on EfficientNetB3 was developed. The model's objective was to differentiate between normal epithelium and OSCC tissues by employing advanced techniques such as data augmentation, regularization, and optimization. Methods: The research utilized a histopathological imaging database for Oral Cancer analysis, incorporating 1224 images from 230 patients. These images, taken at various magnifications, formed the basis for training a specialized deep learning model built upon the EfficientNetB3 architecture. The model underwent training to distinguish between normal epithelium and OSCC tissues, employing sophisticated methodologies including data augmentation, regularization techniques, and optimization strategies. Results: The customized deep learning model achieved significant success, showcasing a remarkable 99% accuracy when tested on the dataset. This high accuracy underscores the model's efficacy in effectively discerning between normal epithelium and OSCC tissues. Furthermore, the model exhibited impressive precision, recall, and F1-score metrics, reinforcing its potential as a robust diagnostic tool for OSCC. Discussion: This research demonstrates the promising potential of employing deep learning models to address the diagnostic challenges associated with OSCC. The model's ability to achieve a 99% accuracy rate on the test dataset signifies a considerable leap forward in earlier and more accurate detection of OSCC. Leveraging advanced techniques in machine learning, such as data augmentation and optimization, has shown promising results in improving patient outcomes through timely and precise identification of OSCC.

Measures of diagnostic precision (repeatability and reproducibility) for three test methods designed to detect spring viremia of carp virus.

Spring viremia of carp virus (SVCV) is a rhabdovirus of the Sprivivirus genus and the etiological agent of an internationally regulated aquatic animal disease in several fish species, including koi carp Cyprinus carpio L. The virus has a complex lifecycle with both acute and persistent stages of infection and can cause high mortality in affected populations. In this study, the diagnostic repeatability (within laboratory agreement) and reproducibility (between laboratory agreement) of 3 tests were investigated to assess their fitness as SVCV diagnostic tools. The tests, reverse transcription quantitative polymerase chain reaction (RT-qPCR) assays targeting either the SVCV glycoprotein (Q1G) or nucleoprotein (Q2N) genes and virus isolation by cell culture (VI), were performed in a blinded study with four Canadian laboratories. Test panels consisted of duplicate sets of 100 tissue samples collected from 3 SVCV prevalence populations of koi: a low-prevalence negative reference population (n = 20 fish) as well as moderate- (n = 50 fish) and high-prevalence (n = 30 fish) populations of koi experimentally infected with SVCV. The Q1G and Q2N tests were performed with kidney tissue in 3 laboratories and with brain tissue in 1 laboratory whereas pools of kidney, spleen and gill tissues were tested with the VI assay in 2 laboratories. Agreement of binary results was evaluated using the observed proportion of agreement, Cohen's kappa and Gwet's agreement coefficient (AC1) whereas the concordance correlation coefficient (ccc) and Bland Altman's limit of agreement were used to evaluate agreement of the RT-qPCR continuous data. Gwet's AC1 provided a more stable estimate of agreement than Cohen's kappa. Overall, high repeatability (AC1, 0.78-0.90) and reproducibility (AC1, 0.74-0.89) were observed for the Q1G and Q2N tests when kidney tissue was used. Lower agreement estimates of repeatability (AC1, 0.54-0.77) and reproducibility (AC1, 0.50-0.80) were obtained for the VI test. RT-qPCR reproducibility was low with kidney-brain tissue pairs (AC1, 0.09-0.46) and high with inter-test pairs of brain (AC1, 0.76-0.86) or kidney tissue (0.75-0.86). Tissue-specific differences in virus load affected test precision and informed final tissue selection. Repeatability (ccc, 0.94-0.97) and reproducibility (ccc, 0.91-0.97) estimates of agreement for paired continuous data from the RT-qPCR assays were similarly high with kidney tissue and lower with paired brain (ccc, 0.15-0.83) and kidney-brain tissues (ccc, 0.01-0.55). The high precision of Q1G and Q2N with kidney tissue suggests that the tests are performing similarly and are suitable candidates for assessment of their diagnostic accuracy.

Bright-field in situ hybridization detects gene alterations and viral infections useful for personalized management of cancer patients.

INTRODUCTION: Bright-field in situ hybridization (ISH) methods detect gene alterations that may improve diagnostic precision and personalized management of cancer patients. Areas covered: This review focuses on some bright-field ISH techniques for detection of gene amplification or viral infection that have already been introduced in tumor pathology, research and diagnostic practice. Other emerging ISH methods, for the detection of translocation, mRNA and microRNA have recently been developed and need both an optimization and analytical validation. The review also deals with their clinical applications and implications on the management of cancer patients. Expert commentary: The technology of bright-field ISH applications has advanced significantly in the last decade. For example, an automated dual-color assay was developed as a clinical test for selecting cancer patients that are candidates for personalized therapy. Recently an emerging bright-field gene-protein assay has been developed. This method simultaneously detects the protein, gene and centromeric targets in the context of tissue morphology, and might be useful in assessing the HER2 status particularly in equivocal cases or samples with heterogeneous tumors. The application of bright-field ISH methods has become the gold standard for the detection of tumor-associated viral infection as diagnostic or prognostic factors.