IspH inhibitors kill Gram-negative bacteria and mobilize immune clearance.

Isoprenoids are vital for all organisms, in which they maintain membrane stability and support core functions such as respiration1. IspH, an enzyme in the methyl erythritol phosphate pathway of isoprenoid synthesis, is essential for Gram-negative bacteria, mycobacteria and apicomplexans2,3. Its substrate, (E)-4-hydroxy-3-methyl-but-2-enyl pyrophosphate (HMBPP), is not produced in metazoans, and in humans and other primates it activates cytotoxic Vγ9Vδ2 T cells at extremely low concentrations4-6. Here we describe a class of IspH inhibitors and refine their potency to nanomolar levels through structure-guided analogue design. After modification of these compounds into prodrugs for delivery into bacteria, we show that they kill clinical isolates of several multidrug-resistant bacteria-including those from the genera Acinetobacter, Pseudomonas, Klebsiella, Enterobacter, Vibrio, Shigella, Salmonella, Yersinia, Mycobacterium and Bacillus-yet are relatively non-toxic to mammalian cells. Proteomic analysis reveals that bacteria treated with these prodrugs resemble those after conditional IspH knockdown. Notably, these prodrugs also induce the expansion and activation of human Vγ9Vδ2 T cells in a humanized mouse model of bacterial infection. The prodrugs we describe here synergize the direct killing of bacteria with a simultaneous rapid immune response by cytotoxic γδ T cells, which may limit the increase of antibiotic-resistant bacterial populations.

Is the liver resilient to the process of ageing?

The liver's unique regenerative capacity, immunotolerant feature, and polyploidy status distinguish it as a metabolic organ unlike any other in the body. Despite aging, the liver generally exhibits fewer pathological abnormalities than other organs (such as the kidney), maintaining its functions near-normal balanced manner. Subtle changes in the liver, including reduced blood flow, detoxification alterations, pseudo-capillarization, and lipofuscin deposition, may occur with chronological age. Research indicates that carefully selected liver grafts from octogenarian donors can perform well post-transplant, emphasizing instances where age doesn't necessarily compromise liver function. Notably, a recent report suggests that the liver is a youthful organ, with hepatocytes averaging an age of only 3 years. Despite the liver's impressive regenerative capabilities and cellular reserve, a lingering question persists: how does the liver maintain its youthful characteristic amidst the chronological aging of the entire organism? The various adaptive mechanism possibly include:(a) cellular hypertrophy to maintain physiological capacity even before proliferation initiates, (b) the "ploidy conveyor" as a genetic adaptation to endure aging-related stress, (c) sustained telomere length indicative of youthfulness (d) active extracellular matrix remodelling for normal cellular functioning, (e) Mitochondria-Endoplasmic Reticulum based metabolic adaptation and (c) cellular plasticity as fitness mechanisms for healthy aging. However, it is crucial to note that aged livers may have compromised regenerative capacity and chronic liver disease is often associated with declining function due to premature hepatocyte senescence. This review delves into varied cellular adaptations sustaining liver homeostasis with chronological aging and briefly explores the role of accelerated hepatocyte aging as a precursor to chronic liver disease.

Tools to measure barriers to medication management capacity in older adults: a scoping review.

BACKGROUND: Medication management capacity is a crucial component of medication adherence, particularly among older adults. Various factors, including physical abilities, cognitive functions, sensory capabilities, motivational, and environmental factors, influence older adults' ability to manage medications. It is, therefore, crucial to identify appropriate tools that allow clinicians to determine which factors may impact medication management capacity and, consequently, nonadherence to medications. PURPOSE: 1)To identify tools that measure physical, cognitive, sensory (vision, hearing, touch), motivational, and environmental barriers to medication self-management in older adults, and 2) to understand the extent to which these tools assess various barriers. METHODS: The scoping review was conducted using Arksey and O'Malley's scoping review framework and the PRISMA Extension for Scoping Reviews checklist. In June 2022, the relevant literature was identified by searching PubMed (MEDLINE), Ovid Embase, Ovid IPA, EBSCOhost CINAHL, APA PsycINFO, and Scopus. RESULTS AND DISCUSSION: In total, 7235 studies were identified. Following the removal of duplicates, 4607 articles were screened by title and abstract, of which 4253 did not meet the inclusion criteria. Three reviewers reviewed the full texts of the remaining 354 articles; among them, 41 articles, 4 theses and 1 conference abstract met the inclusion criteria. From the included studies, 44 tools were identified that measured a combination of physical, cognitive, sensory, motivational, and environmental barriers (n=19) or only cognition (n=13), vision (n=5), environmental factors (n=3), auditory (n=1), and motivational factors (n=1). The review also examined the psychometric properties of the identified tools and found that most of them had reported validity and reliability data. Several tools have demonstrated promise in assessing a combination of barriers with validity and reliability. These tools include the Self-Medication Assessment Tool (SMAT), ManageMed Screening (MMS), Self-Medication Risk Assessment Tool (RAT), HOME-Rx revised, and Medication Management Ability Assessment (MMAA). CONCLUSION: This scoping review identified 44 validated tools to measure various challenges that older adults encounter with medication management. However, no tool measures all five barriers (physical, cognitive, sensory, motivational, and environmental) to medication-taking at home. Therefore, utilizing a combination of tools would be most appropriate to measure these different aspects comprehensively. Further research is needed to develop a new comprehensive tool that simultaneously measures various barriers to medication self-management.

Learning to deep learning: statistics and a paradigm test in selecting a UNet architecture to enhance MRI.

OBJECTIVE: This study aims to assess the statistical significance of training parameters in 240 dense UNets (DUNets) used for enhancing low Signal-to-Noise Ratio (SNR) and undersampled MRI in various acquisition protocols. The objective is to determine the validity of differences between different DUNet configurations and their impact on image quality metrics. MATERIALS AND METHODS: To achieve this, we trained all DUNets using the same learning rate and number of epochs, with variations in 5 acquisition protocols, 24 loss function weightings, and 2 ground truths. We calculated evaluation metrics for two metric regions of interest (ROI). We employed both Analysis of Variance (ANOVA) and Mixed Effects Model (MEM) to assess the statistical significance of the independent parameters, aiming to compare their efficacy in revealing differences and interactions among fixed parameters. RESULTS: ANOVA analysis showed that, except for the acquisition protocol, fixed variables were statistically insignificant. In contrast, MEM analysis revealed that all fixed parameters and their interactions held statistical significance. This emphasizes the need for advanced statistical analysis in comparative studies, where MEM can uncover finer distinctions often overlooked by ANOVA. DISCUSSION: These findings highlight the importance of utilizing appropriate statistical analysis when comparing different deep learning models. Additionally, the surprising effectiveness of the UNet architecture in enhancing various acquisition protocols underscores the potential for developing improved methods for characterizing and training deep learning models. This study serves as a stepping stone toward enhancing the transparency and comparability of deep learning techniques for medical imaging applications.

Light Field View Synthesis Using the Focal Stack and All-in-Focus Image.

Light field reconstruction and synthesis algorithms are essential for improving the lower spatial resolution for hand-held plenoptic cameras. Previous light field synthesis algorithms produce blurred regions around depth discontinuities, especially for stereo-based algorithms, where no information is available to fill the occluded areas in the light field image. In this paper, we propose a light field synthesis algorithm that uses the focal stack images and the all-in-focus image to synthesize a 9 × 9 sub-aperture view light field image. Our approach uses depth from defocus to estimate a depth map. Then, we use the depth map and the all-in-focus image to synthesize the sub-aperture views, and their corresponding depth maps by mimicking the apparent shifting of the central image according to the depth values. We handle the occluded regions in the synthesized sub-aperture views by filling them with the information recovered from the focal stack images. We also show that, if the depth levels in the image are known, we can synthesize a high-accuracy light field image with just five focal stack images. The accuracy of our approach is compared with three state-of-the-art algorithms: one non-learning and two CNN-based approaches, and the results show that our algorithm outperforms all three in terms of PSNR and SSIM metrics.

Pediatric traumatic brain injury and a subsequent transient immune challenge independently influenced chronic outcomes in male mice.

Traumatic brain injury (TBI) is a major contributor to death and disability worldwide. Children are at particularly high risk of both sustaining a TBI and experiencing serious long-term consequences, such as cognitive deficits, mental health problems and post-traumatic epilepsy. Severe TBI patients are highly susceptible to nosocomial infections, which are mostly acquired within the first week of hospitalization post-TBI. Yet the potential chronic impact of such acute infections following pediatric TBI remains unclear. In this study, we hypothesized that a peripheral immune challenge, such as lipopolysaccharide (LPS)-mimicking a hospital-acquired infection-would worsen inflammatory, neurobehavioral, and seizure outcomes after experimental pediatric TBI. To test this, three-week old male C57Bl/6J mice received a moderate controlled cortical impact or sham surgery, followed by 1 mg/kg i.p. LPS (or 0.9% saline vehicle) at 4 days TBI. Mice were randomized to four groups; sham-saline, sham-LPS, TBI-saline or TBI-LPS (n = 15/group). Reduced general activity and increased anxiety-like behavior were observed within 24 h in LPS-treated mice, indicating a transient sickness response. LPS-treated mice also exhibited a reduction in body weights, which persisted chronically. From 2 months post-injury, mice underwent a battery of tests for sensorimotor, cognitive, and psychosocial behaviors. TBI resulted in hyperactivity and spatial memory deficits, independent of LPS; whereas LPS resulted in subtle deficits in spatial memory retention. At 5 months post-injury, video-electroencephalographic recordings were obtained to evaluate both spontaneous seizure activity as well as the evoked seizure response to pentylenetetrazol (PTZ). TBI increased susceptibility to PTZ-evoked seizures; whereas LPS appeared to increase the incidence of spontaneous seizures. Post-mortem analyses found that TBI, but not LPS, resulted in robust glial reactivity and loss of cortical volume. A TBI × LPS interaction in hippocampal volume suggested that TBI-LPS mice had a subtle increase in ipsilateral hippocampus tissue loss; however, this was not reflected in neuronal cell counts. Both TBI and LPS independently had modest effects on chronic hippocampal gene expression. Together, contrary to our hypothesis, we observed minimal synergy between TBI and LPS. Instead, pediatric TBI and a subsequent transient immune challenge independently influenced chronic outcomes. These findings have implications for future preclinical modeling as well as acute post-injury patient management.

Noise-Resilient Depth Estimation for Light Field Images Using Focal Stack and FFT Analysis.

Depth estimation for light field images is essential for applications such as light field image compression, reconstructing perspective views and 3D reconstruction. Previous depth map estimation approaches do not capture sharp transitions around object boundaries due to occlusions, making many of the current approaches unreliable at depth discontinuities. This is especially the case for light field images because the pixels do not exhibit photo-consistency in the presence of occlusions. In this paper, we propose an algorithm to estimate the depth map for light field images using depth from defocus. Our approach uses a small patch size of pixels in each focal stack image for comparing defocus cues, allowing the algorithm to generate sharper depth boundaries. Then, in contrast to existing approaches that use defocus cues for depth estimation, we use frequency domain analysis image similarity checking to generate the depth map. Processing in the frequency domain reduces the individual pixel errors that occur while directly comparing RGB images, making the algorithm more resilient to noise. The algorithm has been evaluated on both a synthetic image dataset and real-world images in the JPEG dataset. Experimental results demonstrate that our proposed algorithm outperforms state-of-the-art depth estimation techniques for light field images, particularly in case of noisy images.

Proteomic Landscape of a Drug-Tolerant Persister Subpopulation of Mycobacterium tuberculosis.

Persisters are a subpopulation of bacteria that resist killing by antibiotics, even though they are genetically similar to their drug-susceptible counterpart. Like in several other bacteria, persisters are also reported in the human pathogen Mycobacterium tuberculosis (Mtb). Stochastic formation of Mtb persisters with a high level of antimicrobial tolerance set the stage for subsequent multidrug-resistant mutations. Despite significant advancement in our understanding, much remains to be learnt about the biology of this drug-recalcitrant bacterial subpopulation. Most of the information pertaining to the metabolic evolution required for emergence of drug tolerance in tuberculosis (TB) pathogens has come from transcriptional, metabolomic, and mutagenesis studies. Since proteins are the key functional molecules regulating the majority of metabolic activities in the cell, investigation of the whole-cell protein expression profile will further provide valuable insights into the physiology of Mtb persisters. We performed a quantitative proteomic analysis of Mtb H37Rv cultured under an in vitro persistence model to identify the proteomic profile of the phenotypic drug-tolerant bacterial population. Our study reveals that proteins related to intermediary metabolism and respiration, cell-wall and cell processes, lipid metabolism, information pathways, and virulence, detoxification and adaptation functional categories are primarily modulated in the persister subpopulation. Further, we demonstrate that various surface-localized mycobacterial membrane protein large (MmpL) proteins, which exhibit a high level of expression in Mtb persisters, are crucial for the mycobacterial survival during persistent growth state. A drug-induced persister subpopulation of Mtb exhibit various differentially regulated proteins that might be critical in mitigating the antimicrobial effect of drugs and can be further explored to develop novel anti-TB agents. The peptide identifications and tandem mass spectra (MS/MS) have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD013621.

A systemic immune challenge to model hospital-acquired infections independently regulates immune responses after pediatric traumatic brain injury.

BACKGROUND: Traumatic brain injury (TBI) is a major cause of disability in young children, yet the factors contributing to poor outcomes in this population are not well understood. TBI patients are highly susceptible to nosocomial infections, which are mostly acquired within the first week of hospitalization, and such infections may modify TBI pathobiology and recovery. In this study, we hypothesized that a peripheral immune challenge such as lipopolysaccharide (LPS)-mimicking a hospital-acquired infection-would worsen outcomes after experimental pediatric TBI, by perpetuating the inflammatory immune response. METHODS: Three-week-old male mice received either a moderate controlled cortical impact or sham surgery, followed by a single LPS dose (1 mg/kg i.p.) or vehicle (0.9% saline) at 4 days post-surgery, then analysis at 5 or 8 days post-injury (i.e., 1 or 4 days post-LPS). RESULTS: LPS-treated mice exhibited a time-dependent reduction in general activity and social investigation, and increased anxiety, alongside substantial body weight loss, indicating transient sickness behaviors. Spleen-to-body weight ratios were also increased in LPS-treated mice, indicative of persistent activation of adaptive immunity at 4 days post-LPS. TBI + LPS mice showed an impaired trajectory of weight gain post-LPS, reflecting a synergistic effect of TBI and the LPS-induced immune challenge. Flow cytometry analysis demonstrated innate immune cell activation in blood, brain, and spleen post-LPS; however, this was not potentiated by TBI. Cytokine protein levels in serum, and gene expression levels in the brain, were altered in response to LPS but not TBI across the time course. Immunofluorescence analysis of brain sections revealed increased glia reactivity due to injury, but no additive effect of LPS was observed. CONCLUSIONS: Together, we found that a transient, infection-like systemic challenge had widespread effects on the brain and immune system, but these were not synergistic with prior TBI in pediatric mice. These findings provide novel insight into the potential influence of a secondary immune challenge to the injured pediatric brain, with future studies needed to elucidate the chronic effects of this two-hit insult.

Infections after a traumatic brain injury: The complex interplay between the immune and neurological systems.

Traumatic brain injury (TBI) is a serious global health issue, being the leading cause of death and disability for individuals under the age of 45, and one of the largest causes of global neurological disability. In addition to the brain injury itself, it is increasingly appreciated that a TBI may also alter the systemic immune response in a way that renders TBI patients more vulnerable to infections in the acute post-injury period. Such infections pose an additional challenge to the patient, increasing rates of mortality and morbidity, and worsening neurological outcomes. Hospitalization, surgical interventions, and a state of immunosuppression induced by injury to the central nervous system (CNS), may all contribute to the high rate of infections seen in the population with TBI. Ongoing research to better understand the immunomodulators that underlie TBI-induced immunosuppression may aid in the development of effective therapeutic strategies to improve the recovery trajectory for patients. This review first describes the clinical scenario, posing the question of whether TBI patients are more susceptible to infections such as pneumonia, and if so, why? We then consider how cross-talk between the injured brain and the systemic immune system occurs, and further, how the additional immune challenge of an acquired infection can contribute to ongoing neuroinflammation and neurodegeneration after a TBI. Experimental models combining TBI with infection are discussed, as well as current treatment options available for this double-barreled insult. The aims of this review are to summarize current understanding of the bidirectional relationship between the CNS and the immune system when faced with a mechanical trauma combined with a concomitant infection, and to highlight key outstanding questions that remain in the field.

Silver Doped Graphitic Carbon Nitride for the Enhanced Photocatalytic Activity Towards Organic Dyes.

Recently, graphitic carbon nitride has been investigated as a promising photocatalyst for organic dye degradation application. In this study, a facile strategy to synthesise silver nanoparticles (AgNPs) doped graphitic carbon nitride (GCN-Ag) has been reported. The characterisation study of the asprepared samples was performed using various analytical techniques. The X-ray diffraction (XRD) and fourier transform infrared spectroscopy (FTIR) revealed that the structure of pure graphitic carbon nitride (GCN-Pure) partly changed on the addition of the AgNPs. The diffused reflectance spectra (DRS) unveiled a significant red shift in the absorption edge of GCN-Ag. The scanning electron microscopy (SEM) analysis revealed that the morphological aspects of GCN-Pure changed on the addition of AgNPs. Further the as-prepared samples have been compared for their degradation activity towards organic dye pollutants including methylene blue, crystal violet and rose bengal. The phenomenon of the better separation of photogenerated charge carriers was attributed to the better photoactivity in the case of GCN-Ag than GCN-Pure. In addition to it the reusability experiment of GCN-Ag revealed that the catalyst remained highly stable after the three cyclic runs of photodegradation experiment.

A step towards intelligent EBSD microscopy: machine-learning prediction of twin activity in MgAZ31.

Although microscopy is often treated as a quasi-static exercise for obtaining a snapshot of events and structure, it is clear that a more dynamic approach, involving real-time decision making for guiding the investigation process, may provide deeper insights, more efficiently. On the other hand, many applications of machine learning involve the interpretation of local circumstances from experience gained over many observations; that is, machine learning potentially provides an ideal solution for more efficient microscopy. This paper explores the potential for informing the microscope's observation strategy while characterising critical events. In particular, the identification of regions likely to experience twin activity (twin interaction with grain boundary) in AZ31 magnesium is attempted, from only local information. EBSD-based observations in the neighbourhoods of twin activity are fed into a machine-learning environment to inform the future search for such events, and the accuracy of the resultant decisions is quantified relative to the number of prior observations. The potential for utilising different types of local information, and their resultant value in the prediction process, is also assessed. After applying an attribute selection filter, and various other machine-learning tools, a decision-tree model is able to classify likely neighbourhoods of twin activity with 85% accuracy. The resultant framework provides the first step towards an intelligent microscopy for efficient observation of stochastic events during in situ microscopy campaigns. LAY DESCRIPTION: One role of artificial intelligence is to predict future events after learning from many previous observations. In materials science, various phenomena (such as crack nucleation) are difficult to predict because they have been insufficiently observed. Furthermore, observation is difficult, precisely because their location cannot be predicted, leading to a chicken and egg conundrum. This paper applies machine learning to the search for twin nucleation sites in a magnesium alloy, in an attempt to guide the observation of twin nucleation events in a microscope based on previous observations. As more data is obtained, the accuracy of the location prediction will increase. In the current case, the machine-learning tool achieved 85% accuracy for predicting the location of twin interactions with grain boundaries after several thousand observations. The resultant framework provides the first step towards an intelligent microscopy for efficient observation of stochastic events during in situ microscopy campaigns.

Biochemical and functional characterization of MRA_1571 of Mycobacterium tuberculosis H37Ra and effect of its down-regulation on survival in macrophages.

Amino acid biosynthesis has emerged as a source of new drug targets as many bacterial strains auxotrophic for amino acids fail to proliferate under in vivo conditions. Branch chain amino acids (BCAAs) are important for Mycobacterium tuberculosis (Mtb) survival and strains deficient in their biosynthesis were attenuated for growth in mice. Threonine dehydratase (IlvA) is a pyridoxal-5-phosphate (PLP) dependent enzyme that catalyzes the first step in isoleucine biosynthesis. The MRA_1571 of Mycobacterium tuberculosis H37Ra (Mtb-Ra), annotated to be coding for IlvA, was cloned, expressed and purified. Purified protein was subsequently used for developing enzyme assay and to study its biochemical properties. Also, E. coli BL21 (DE3) IlvA knockout (E. coli-ΔilvA) was developed and genetically complemented with Mtb-Ra ilvA expression construct (pET32a-ilvA) to make complemented E. coli strain (E. coli-ΔilvA + pET32a-ilvA). The E. coli-ΔilvA showed growth failure in minimal medium but growth restoration was observed in E. coli-ΔilvA + pET32a-ilvA. E. coli-ΔilvA growth was also restored in the presence of isoleucine. The IlvA localization studies detected its distribution in cell wall and membrane fractions with relatively minor presence in cytosolic fraction. Maximum IlvA expression was observed at 72 h in wild-type (WT) Mtb-Ra infecting macrophages. Also, Mtb-Ra IlvA knockdown (KD) showed reduced survival in macrophages compared to WT and complemented strain (KDC).

Malignant Melanoma of the Oral Cavity: An Aggressive and Rare Entity.

It is exceptionally unusual to see oral malignant melanoma, which is highly aggressive with dismal prognosis. A 61-year-old female patient presented with hyperpigmented and hemorrhagic areas in the anterior mandible. Microscopically, positive Immunohistochemistry staining with monoclonal antibody Human Melanoma Black (HMB45) and S100 protein, confirmed the diagnosis as malignant melanoma.

Clear cell oral squamous cell carcinoma as a diagnostic conundrum: report of 2 rare cases.

The occurrence of clear cell histologic sub-type of oral squamous cell carcinoma in the oral cavity is a distinct and exceedingly rare entity exhibiting aggressive behavior. To date, only 10 cases have been published in the literature. We describe 2 extremely rare cases, both presenting with swelling and ulcerated nodule-like proliferative growth in the mandible. Microscopically, sheets and lobules of neoplastic squamous epithelial cells showing clear cell differentiation were appreciated in both patients. Periodic acid-Schiff and mucicarmine revealed negative staining. Immunohistochemical (IHC) analysis for antibody for renal cell tumor marker CD 10 was immune-negative. The malignant clear cells in both cases showed intense positive reactions with IHC markers pan-cytokeratin and epithelial membrane antigen, confirming the diagnosis as a clear cell variant of oral squamous cell carcinoma (CCOSCC). The first patient was unwilling for treatment and eventually died within 2 months of the diagnosis. In the second patient, right hemi-mandibulectomy with level 1A and 1B lymph nodes was performed. Adjuvant chemotherapy with low-dose methotrexate was initiated. Follow-up after 2 months of surgery was uneventful. Current rare reports emphasize the significance of prompt and extensive diagnostic work-up of clear cell neoplasms, as the CCOSCC may be fatal.

Let UNet Play an Adversarial Game: Investigating the Effect of Adversarial Training in Enhancing Low-Resolution MRI.

Adversarial training has attracted much attention in enhancing the visual realism of images, but its efficacy in clinical imaging has not yet been explored. This work investigated adversarial training in a clinical context, by training 206 networks on the OASIS-1 dataset for improving low-resolution and low signal-to-noise ratio (SNR) magnetic resonance images. Each network corresponded to a different combination of perceptual and adversarial loss weights and distinct learning rate values. For each perceptual loss weighting, we identified its corresponding adversarial loss weighting that minimized structural disparity. Each optimally weighted adversarial loss yielded an average SSIM reduction of 1.5%. We further introduced a set of new metrics to assess other clinically relevant image features: Gradient Error (GE) to measure structural disparities; Sharpness to compute edge clarity; and Edge-Contrast Error (ECE) to quantify any distortion of the pixel distribution around edges. Including adversarial loss increased structural enhancement in visual inspection, which correlated with statistically consistent GE reductions (p-value << 0.05). This also resulted in increased Sharpness; however, the level of statistical significance was dependent on the perceptual loss weighting. Additionally, adversarial loss yielded ECE reductions for smaller perceptual loss weightings, while showing non-significant increases (p-value >> 0.05) when these weightings were higher, demonstrating that the increased Sharpness does not adversely distort the pixel distribution around the edges in the image. These studies clearly suggest that adversarial training significantly improves the performance of an MRI enhancement pipeline, and highlights the need for systematic studies of hyperparameter optimization and investigation of alternative image quality metrics.

An Unusual Case of Symptomatic Isolated Lingual Cysticercosis: Clinical Suspicion Helped Prevent Disseminated Disease.

Intraorally, cysticercosis is regarded as uncommon and a diagnostic challenge. Here, we report a diagnostic conundrum of an unusual case of innocuous appearing lesion on the tongue presenting as moderately tender swelling finally diagnosed as lingual cysticercosis, based on USG (Ultrasound), CT (Computed Tomography) findings and characteristic histopathologic features.

Recent nanotheranostic approaches in cancer research.

Humanity is suffering from cancer which has become a root cause of untimely deaths of individuals around the globe in the recent past. Nanotheranostics integrates therapeutics and diagnostics to monitor treatment response and enhance drug efficacy and safety. We hereby propose to discuss all recent cancer imaging and diagnostic tools, the mechanism of targeting tumor cells, and current nanotheranostic platforms available for cancer. This review discusses various nanotheranostic agents and novel molecular imaging tools like MRI, CT, PET, SPEC, and PAT used for cancer diagnostics. Emphasis is given to gold nanoparticles, silica, liposomes, dendrimers, and metal-based agents. We also highlight the mechanism of targeting the tumor cells, and the limitations of different nanotheranostic agents in the field of research for cancer treatment. Due to the complexity in this area, multifunctional and hybrid nanoparticles functionalized with targeted moieties or anti-cancer drugs show the best feature for theranostics that enables them to work on carrying and delivering active materials to the desired area of the requirement for early detection and diagnosis. Non-invasive imaging techniques have a specificity of receptor binding and internalization processes of the nanosystems within the cancer cells. Nanotheranostics may provide the appropriate medicine at the appropriate dose to the appropriate patient at the appropriate time.

Use of Potentially Inappropriate Medications among Older Adults with Dementia or Cognitive Impairment Attending Memory Clinics: A Protocol for a Systematic Review and Meta-Analysis.

Introduction: Older adults with dementia who are on multiple medications are more vulnerable to the use of potentially inappropriate medications (PIMs), which can significantly increase the risk of adverse events and drug-related problems. PIMs use is prevalent and varies among older adults with dementia or cognitive impairment (CI) attending memory clinics. However, the prevalence of PIMs, polypharmacy, and hyper-polypharmacy among older adults with dementia or CI who are attending memory clinics is not well understood. We will conduct a systematic review and meta-analyses to examine the overall estimate of the prevalence of the PIMs, polypharmacy, and hyper-polypharmacy use among older adults attending memory clinics, with dementia or CI. The secondary objective of this study will be to compile a list of commonly implicated PIMs and to investigate factors that may be associated with using PIMs in this population. Methods: Ovid MEDLINE, Ovid Embase, Scopus, Cochrane library, EBSCOhost CINAHL, and Ovid International Pharmaceutical Abstracts (IPA) will be systematically searched by a researcher (R.S.) with the help of a librarian (C.C.). All databases will be searched from inception to May 05, 2023. Cross-sectional, cohort, randomized clinical trials, quasi-experimental, and case-control studies will be included if they assess PIM's use among older adults with dementia and/or CI. A step-by-step guide by Pai et al. [Natl Med J India. 2004;17(2):86-95] will be followed when conducting this systematic review (S.R.). The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist will be followed for reporting this SR. Conclusion: The findings from this SR/MA will identify the pooled prevalence of PIMs, providing a more precise estimate of the true prevalence of the PIMs, polypharmacy, hyper-polypharmacy in older adults with dementia or CI who are attending memory clinics at primary, secondary, or tertiary healthcare settings by considering the results of multiple studies.