A Reconfigurable DNA Framework Nanotube-Assisted Antiangiogenic Therapy.

A major limitation of tumor antiangiogenic therapy is the pronounced off-target effect, which can lead to unavoidable injury in multiple organs. Ensuring sufficient delivery and controlled release of these antiangiogenic agents at tumor sites is crucial for realizing their clinical application. Here, we develop a smart DNA-based nanodrug, termed Endo-rDFN, by precisely assembling the antiangiogenic agent, endostar (Endo), into a reconfigurable DNA framework nanotube (rDFN) that could recognize tumor-overexpressed nucleolin to achieve the targeted delivery and controllable release of Endo. Endo-rDFN can not only effectively enhance the tumor-targeting capability of Endo and maintain its efficient accumulation in tumor tissues but also achieve on-demand release of Endo at tumor sites via the specific DNA aptamer for tumor-overexpressed nucleolin, named AS1411. We also found that Endo-rDFN exhibited significant inhibition of angiogenesis and tumor growth, while also providing effective protection against multiorgan injury (heart, liver, spleen, kidney, lung, etc.) to some extent, without compromising the function of these organs. Our study demonstrates that rDFN represents a promising vector for reducing antiangiogenic therapy-induced multiorgan injury, highlighting its potential for promoting the clinical application of antiangiogenic agents.

AI-based differential diagnosis of dementia etiologies on multimodal data.

Differential diagnosis of dementia remains a challenge in neurology due to symptom overlap across etiologies, yet it is crucial for formulating early, personalized management strategies. Here, we present an AI model that harnesses a broad array of data, including demographics, individual and family medical history, medication use, neuropsychological assessments, functional evaluations, and multimodal neuroimaging, to identify the etiologies contributing to dementia in individuals. The study, drawing on 51,269 participants across 9 independent, geographically diverse datasets, facilitated the identification of 10 distinct dementia etiologies. It aligns diagnoses with similar management strategies, ensuring robust predictions even with incomplete data. Our model achieved a micro-averaged area under the receiver operating characteristic curve (AUROC) of 0.94 in classifying individuals with normal cognition, mild cognitive impairment and dementia. Also, the micro-averaged AUROC was 0.96 in differentiating the dementia etiologies. Our model demonstrated proficiency in addressing mixed dementia cases, with a mean AUROC of 0.78 for two co-occurring pathologies. In a randomly selected subset of 100 cases, the AUROC of neurologist assessments augmented by our AI model exceeded neurologist-only evaluations by 26.25%. Furthermore, our model predictions aligned with biomarker evidence and its associations with different proteinopathies were substantiated through postmortem findings. Our framework has the potential to be integrated as a screening tool for dementia in various clinical settings and drug trials, with promising implications for person-level management.

[Coordinated Control of Carbon Emission Reduction and Air Quality Improvement in the Industrial Sector in Hunan Province].

Climate warming and air pollution are the main environmental problems in China. This study used China's Carbon Accounting Database, energy economic model, and air quality model to analyze the potential carbon emission peaking path and synergistic air quality improvement gain in the industrial sector in Hunan Province. Based on China's Carbon Accounting Database and the local industry/energy statistical yearbooks in Hunan, the total CO2 emissions in Hunan Province in 2019 were 310.6 Mt, of which the industrial sector accounted for over 70% of the emissions, mainly from the production and supply of electricity, steam, and heat; the production of non-metallic minerals; and the smelting and pressing of ferrous metals. Three potential industrial carbon emission peaking scenarios were analyzed using the LEAP energy economic model, including the business-as-usual scenario (peaking by 2030), moderate emission reduction scenario (peaking by 2028), and aggressive emission reduction scenario (peaking by 2025), by employing different economic growth rates, energy technology progress, and energy structures of the industrial sector. Furthermore, by combining the anthropogenic air pollutant emission inventory and the regional air quality model WRF-Chem, we analyzed the air quality improvement associated with various carbon emission peak paths. The results showed that the annual mean concentrations of major air pollutants had decreased in the three scenarios, especially in the Chang-Zhu-Tan Region. The aggressive emission reduction scenario was the most effective scenario, followed by the moderate emission reduction scenario and the business-as-usual scenario. Manufacturing was the sector with the most significant synergistic effect of pollution and carbon reduction. When carbon emission peaks were achieved, the annual average concentrations of PM2.5 and PM10 in Hunan Province could be synergistically reduced by 0.6-1.8 µg·m-3 and 1.8-8.9 µg·m-3, respectively. Our findings offer important insights into carbon emission peaking and can provide useful information for potential mitigation actions.

Preparation of amino-functionalized fly ash based tobermorite for enhanced removal of Cr(VI).

Amino-functionalized fly ash based tobermorite (NH2-FAT) was prepared via a two-step process including a hydrothermal preparation of fly ash based tobermorite (FAT) from fly ash (FA) and the impregnation of 3-aminopropyltriethoxysilane (APTES). The characteristics of FA, FAT, and NH2-FAT were systematically measured. The removal capacity of Cr(VI) by FAT and NH2-FAT were comparatively studied. The results indicated that the NH2-FAT presented excellent removal efficiency towards Cr(VI) in the condition of pH of 2. The adsorption capacity of Cr(VI) by NH2-FAT in this study was up to 498.39 mg/g resulted from Langmuir model, which was more superior to that of FAT, other reported FA based adsorbents and most amino-functionalized adsorbents. Furthermore, the removal mechanisms of Cr(VI) by NH2-FAT were ascribed to both electrostatic attraction and reduction to Cr(III) by amino groups. Overall, this work indicates the NH2-FAT is a promising adsorbent for treating wastewater containing Cr(VI), and provides a new utilization method of FA.

Identifying CDC7 as a synergistic target of chemotherapy in resistant small-cell lung cancer via CRISPR/Cas9 screening.

There is currently a lack of efficacious treatments for patients with chemo-resistant small-cell lung cancer (SCLC), leading to poor prognoses. We examined a chemo-resistant SCLC cell line using genome-wide CRISPR/Cas9 screening and identified serine/threonine kinase cell division cycle 7 (CDC7) as a potential synergistic target. Silencing CDC7 in chemo-resistant SCLC cells decreased the IC50 and improved the efficacy of chemotherapy. Based on the highest single agent model, the CDC7 inhibitor XL413 had a synergistic effect with both cisplatin and etoposide in chemo-resistant SCLC cells, but had no such effect in chemo-sensitive SCLC cells; the combination of XL413 and chemotherapy significantly inhibited cell growth. Western blot and flow cytometry showed that the combined treatments increased apoptosis, whereas XL413 alone had little effect on apoptosis. An analysis of cell cycle and cyclin protein levels indicated that the combination of XL413 and chemotherapy-induced G1/S phase arrest and DNA damage in chemo-resistant SCLC cells. Xenografted tumor and histoculture drug response assays using patient-derived xenografts showed that XL413 improved the efficacy of chemotherapy in vivo and with SCLC tissues. These results suggest that XL413 exerts a synergistic effect with chemotherapy on chemo-resistant SCLC.

Secondary PM2.5 dominates aerosol pollution in the Yangtze River Delta region: Environmental and health effects of the Clean air Plan.

The Clean Air Plan has been active in China since 2013 to mitigate severe PM2.5 pollution. In this study, we applied the air quality model WRF-Chem to simulate PM2.5 in the Yangtze River Delta (YRD) region of China in 2017, with the aim of assessing the air quality improvement and its associated health burden in the final year of the Clean Air Plan. To better describe the fate of various PM2.5 compositions, we updated the chemical mechanisms in the model beforehand, including heterogeneous sulfate reactions, aqueous secondary organic aerosol (SOA) uptake, and volatility basis set (VBS) based SOA production. Both the observation and simulation results agreed that the stringent clear air action effectively reduced the PM2.5 pollution levels by âˆ¼ 30 %. The primary PM2.5 (-6 âˆ¼  - 16 % yr-1) showed a more significant decreasing trend than the secondary PM2.5 (-2 âˆ¼  - 8 % yr-1), which was mainly caused by the directivity of the clear air actions and the worsening ozone pollution in the recent years. The inconsistent decreasing trends of PM2.5 components subsequently led to an increasing proportion of secondary PM2.5. Nitrate particles, higher in the central and western YRD region, have replaced sulfate and have become the largest component of secondary inorganic aerosols year-round, except in summer, when strong ammonium nitrate evaporation occurs. In addition, SOA remains an important component (21 âˆ¼ 22 %) especially in summer, most of which is produced from the oxidation and ageing of semi/intermediate volatile organic compounds (S/IVOC). Furthermore, we quantified the associated health impacts and found that the Clean Air Plan has largely reduced premature mortality due to PM2.5 exposure in the YRD region from 399.1 thousand to 295.7 thousand. Our study highlights the benefits of the Clean Air Plan and suggests that subsequent PM2.5 improvement should be geared more towards controlling secondary pollutants.

Multimodal deep learning for Alzheimer's disease dementia assessment.

Worldwide, there are nearly 10 million new cases of dementia annually, of which Alzheimer's disease (AD) is the most common. New measures are needed to improve the diagnosis of individuals with cognitive impairment due to various etiologies. Here, we report a deep learning framework that accomplishes multiple diagnostic steps in successive fashion to identify persons with normal cognition (NC), mild cognitive impairment (MCI), AD, and non-AD dementias (nADD). We demonstrate a range of models capable of accepting flexible combinations of routinely collected clinical information, including demographics, medical history, neuropsychological testing, neuroimaging, and functional assessments. We then show that these frameworks compare favorably with the diagnostic accuracy of practicing neurologists and neuroradiologists. Lastly, we apply interpretability methods in computer vision to show that disease-specific patterns detected by our models track distinct patterns of degenerative changes throughout the brain and correspond closely with the presence of neuropathological lesions on autopsy. Our work demonstrates methodologies for validating computational predictions with established standards of medical diagnosis.

Headache Emergencies.

Headache is a common reason for seeking medical attention. Most cases are benign primary headache disorders; however, there is significant overlap between symptoms of these disorders and secondary headaches. Differentiating these clinical scenarios requires a careful history with attention to red flag symptoms and a neurologic examination. These details can identify dangerous disorders: subarachnoid hemorrhage, reversible cerebral vasoconstriction syndrome, elevated intracranial pressure, hydrocephalus, cerebral venous sinus thrombosis, arterial dissection, central nervous system infection, and inflammatory vasculitis. Older, pregnant, or immunocompromised patients have a higher risk for secondary disorders; clinicians should have a different threshold to conduct evaluations in such patients.

The Effects of Video Instruction on Neuroscience Intensive Care Unit Nursing Skills in Case Presentations and Neurological Examinations.

ABSTRACT: BACKGROUND: The emergence of neuroscience intensive care units (NSICUs) for the past decades has led to growing interest in targeted training for NSICU nurses. We sought to evaluate the use of video instruction on NSICU nurses' skills in case presentations and neurological examinations, which has timely advantages as an asynchronous and distanced learning modality. METHODS: We enrolled NSICU and surgical intensive care unit nurses who took shifts in the NSICU at our institution. Participants were observed by a neurocritical care attending physician presenting the clinical details of an admitted patient and conducting a neurological examination, with both parties completing a 10-item evaluation on NSICU nursing presentation and examination skills. Participants randomized to an intervention group were given access to an instructional video on NSICU nursing skills. A median of 21 days later, participants were observed by a physician blinded to study randomization, with both parties recompleting the evaluation. Differences between day 1 and day 21 scores were analyzed using paired sample t tests. RESULTS: Fifteen NSICU and 55 surgical intensive care unit nurses were enrolled. Surgical intensive care unit nurses in both the intervention and control groups had statistically significant improvement between day 1 and day 21 physician-rated scores, with a greater increase in the intervention group; self-rated scores did not change. For NSICU nurses, there were no differences in physician-rated or self-rated scores for either group. CONCLUSIONS: Surgical intensive care unit nurses who underwent direct observation and self-evaluation had improvement in physician-rated NSICU nursing skills, likely as these activities allowed for reflective observation in Kolb's experiential learning cycle. Greater improvement in participants who viewed an instructional video highlights its value as a teaching modality for nurses.

Development and validation of an interpretable deep learning framework for Alzheimer's disease classification.

Alzheimer's disease is the primary cause of dementia worldwide, with an increasing morbidity burden that may outstrip diagnosis and management capacity as the population ages. Current methods integrate patient history, neuropsychological testing and MRI to identify likely cases, yet effective practices remain variably applied and lacking in sensitivity and specificity. Here we report an interpretable deep learning strategy that delineates unique Alzheimer's disease signatures from multimodal inputs of MRI, age, gender, and Mini-Mental State Examination score. Our framework linked a fully convolutional network, which constructs high resolution maps of disease probability from local brain structure to a multilayer perceptron and generates precise, intuitive visualization of individual Alzheimer's disease risk en route to accurate diagnosis. The model was trained using clinically diagnosed Alzheimer's disease and cognitively normal subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (n = 417) and validated on three independent cohorts: the Australian Imaging, Biomarker and Lifestyle Flagship Study of Ageing (AIBL) (n = 382), the Framingham Heart Study (n = 102), and the National Alzheimer's Coordinating Center (NACC) (n = 582). Performance of the model that used the multimodal inputs was consistent across datasets, with mean area under curve values of 0.996, 0.974, 0.876 and 0.954 for the ADNI study, AIBL, Framingham Heart Study and NACC datasets, respectively. Moreover, our approach exceeded the diagnostic performance of a multi-institutional team of practicing neurologists (n = 11), and high-risk cerebral regions predicted by the model closely tracked post-mortem histopathological findings. This framework provides a clinically adaptable strategy for using routinely available imaging techniques such as MRI to generate nuanced neuroimaging signatures for Alzheimer's disease diagnosis, as well as a generalizable approach for linking deep learning to pathophysiological processes in human disease.

Migraine and Tension-Type Headache.

Migraine and tension-type headache (TTH) are common primary disorders that carry significant morbidity and socioeconomic effect. In this article, we will review the epidemiology, presentation, and diagnosis of these disorders. First-line acute treatment for migraine consists of analgesics, triptans, and antiemetics, while nonsteroidal anti-inflammatory drugs are the mainstay treatment for TTH. Patients with frequent or chronic headaches warrant prophylactic therapy. For migraine, various classes of preventives can be used (ß-blockers, tricyclics, antiepileptics, botulinum toxin), with the choice of therapy tailored to the patient's risk factors and symptoms. For TTH, tricyclics have the most evidence as prophylactic therapy. A new class of medication, monoclonal antibodies to calcitonin gene receptor peptide or its receptor, became available in 2018, and is the first class of medication specifically designed to treat migraine. In addition to pharmacotherapy, we will also review nonpharmacologic interventions as well as neuromodulation for migraine.

CADASIL: Imaging Characteristics and Clinical Correlation.

Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is caused by mutations in the NOTCH3 gene located on chromosome 19p13. CADASIL causes a clinical syndrome of migraines (frequently with aura), progressive strokes, and cognitive decline in adults leading to severe functional impairment by the seventh decade of life. Genetic testing is the gold standard for diagnosing this condition, but the syndrome can be suspected clinically based on history and a characteristic pattern of confluent subcortical white matter disease in the anterior temporal poles and external capsule. Additional abnormalities include cerebral microbleeds and large vessel stenosis, particularly in Asian populations. Familiarity with radiologic findings in CADASIL is essential to the correct diagnosis and subsequent management of this disease.

Non-Invasive Neuromodulation for Headache Disorders.

Migraine and other chronic headache disorders are common and if inadequately treated, can lead to significant disability. The effectiveness of medications can be limited by side effects, drug interactions, and comorbid diseases necessitating alternative methods. Technological developments in the past 5 years have made it possible to use non-invasive methods of neuromodulation to treat primary headache disorders. This field includes technologies such as supraorbital transcutaneous stimulation (STS), transcranial magnetic stimulation (TMS), and non-invasive vagal nerve stimulation (nVNS). Existing trials show these modalities are safe and well tolerated and can be combined with standard pharmacotherapy. We review the technologies, biological rationales, and trials involving non-invasive neuromodulation for the treatment of primary headache disorders.

Recurrent vascular events in lacunar stroke patients with metabolic syndrome and/or diabetes.

OBJECTIVES: We used a prospective clinical trial to examine the risks conferred by metabolic syndrome (METS) and diabetes mellitus (DM) to recurrent strokes in the Secondary Prevention of Small Subcortical Strokes (SPS3) study cohort. METHODS: The SPS3 trial enrolled 3,020 patients with lacunar strokes. Participants were stratified into groups of METS only, DM only, both, or neither using American Heart Association/National Heart, Lung, and Blood Institute and World Health Organization guidelines. Annualized event rates of strokes, myocardial infarction (MI), and all-cause mortality were calculated, and hazard ratios (HRs) referencing the "neither" group were computed, controlling for significantly associated baseline characteristics. RESULTS: Among 2,999 participants, 25% had METS only, 6% had DM only, 32% had both conditions, and 37%had neither. Over a median of 3.8 years of follow-up, there were 274 recurrent strokes (240 ischemic, 34 hemorrhagic) and 74 MIs; among the 240 ischemic strokes, 134 (56%) were lacunar. The rates of any recurrent stroke (HR 1.7, 95% confidence interval [CI] 1.3­2.3) or lacunar stroke (HR 2.4, 95% CI 1.5­3.7) were significantly higher for those with concurrent METS and DM compared with those who had neither. Risk of incident MI was higher in participants with DM (HR 2.8, 95% CI 1.1­7.0) or concurrent DM and METS (HR 2.6, 95% CI 1.4­4.9). CONCLUSION: METS and DM were significant comorbid conditions in lacunar stroke patients and they were associated with stroke recurrence. In patients with lacunar infarcts, a vigilant approach to prevent development of DM in those with METS may be a potential strategy to reduce recurrent strokes.

When indomethacin fails: additional treatment options for "indomethacin responsive headaches".

Indomethacin has been used for the treatment of headache disorders since the 1960's, shortly after it was introduced as a treatment for pain and joint swelling in rheumatologic conditions. A subgroup of primary headache disorders, often refractory to other pharmacologic treatment such as triptans and the usual non-steroidal anti-inflammatories, was noted to be exquisitely and absolutely responsive to the analgesic effects of indomethacin. These disorders have been better characterized over the past decade and classified into primary headache disorders of paroxysmal hemicrania (PH) and hemicrania continua (HC). Since the current ICHD-3 beta requires response to indomethacin as a diagnostic criterion, studies on alternative treatments in HC and PH generally occur in patients with intolerance to its gastro-intestinal side effects rather than loss of analgesia effectiveness. More rarely, the development of new headaches have been reported in chronic indomethacin use. In these settings, other classes of medications such as selective cyclooxygenase-2 inhibitors (celecoxib), anti-epileptic agents (topiramate), calcium channel blockers (verapamil, flunarizine), melatonin, and local nerve blocks with anesthetic and steroids have been shown to be effective in case reports and series. We review the literature and provide our clinical recommendations on alternative therapies for the "indomethacin-responsive headaches".

Ramsay Hunt syndrome type II.

A 57-year-old man developed 3 days of left facial pain and swelling with left-sided hearing loss followed by a painful, unilateral, erythematous, and vesicular rash on the left anterior two-thirds of the tongue, external auditory canal, lip, and face typical of varicella-zoster virus reactivation (figure). Reactivation in the geniculate ganglion or facial nerve is uncommon and typically causes tongue and auricular lesions or facial palsy and was described by Hunt in 1907.(1) The patient received IV acyclovir and oral prednisone with rapid improvement in pain and resolution of lesions and improvement of hearing over 1 month.