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A Multi-Modal Foundation Model to Assist People with Blindness and Low Vision in Environmental Interaction

Hao, Yu; Yang, Fan; Huang, Hao; Yuan, Shuaihang; Rangan, Sundeep; Rizzo, John Ross; Wang, Yao; Fang, Yi
People with blindness and low vision (pBLV) encounter substantial challenges when it comes to comprehensive scene recognition and precise object identification in unfamiliar environments. Additionally, due to the vision loss, pBLV have difficulty in accessing and identifying potential tripping hazards independently. Previous assistive technologies for the visually impaired often struggle in real-world scenarios due to the need for constant training and lack of robustness, which limits their effectiveness, especially in dynamic and unfamiliar environments, where accurate and efficient perception is crucial. Therefore, we frame our research question in this paper as: How can we assist pBLV in recognizing scenes, identifying objects, and detecting potential tripping hazards in unfamiliar environments, where existing assistive technologies often falter due to their lack of robustness? We hypothesize that by leveraging large pretrained foundation models and prompt engineering, we can create a system that effectively addresses the challenges faced by pBLV in unfamiliar environments. Motivated by the prevalence of large pretrained foundation models, particularly in assistive robotics applications, due to their accurate perception and robust contextual understanding in real-world scenarios induced by extensive pretraining, we present a pioneering approach that leverages foundation models to enhance visual perception for pBLV, offering detailed and comprehensive descriptions of the surrounding environment and providing warnings about potential risks. Specifically, our method begins by leveraging a large-image tagging model (i.e., Recognize Anything Model (RAM)) to identify all common objects present in the captured images. The recognition results and user query are then integrated into a prompt, tailored specifically for pBLV, using prompt engineering. By combining the prompt and input image, a vision-language foundation model (i.e., InstructBLIP) generates detailed and comprehensive descriptions of the environment and identifies potential risks in the environment by analyzing environmental objects and scenic landmarks, relevant to the prompt. We evaluate our approach through experiments conducted on both indoor and outdoor datasets. Our results demonstrate that our method can recognize objects accurately and provide insightful descriptions and analysis of the environment for pBLV.
SCOPUS:85194155730
ISSN: 2313-433x
CID: 5659742

A Multi-Modal Foundation Model to Assist People with Blindness and Low Vision in Environmental Interaction

Hao, Yu; Yang, Fan; Huang, Hao; Yuan, Shuaihang; Rangan, Sundeep; Rizzo, John-Ross; Wang, Yao; Fang, Yi
People with blindness and low vision (pBLV) encounter substantial challenges when it comes to comprehensive scene recognition and precise object identification in unfamiliar environments. Additionally, due to the vision loss, pBLV have difficulty in accessing and identifying potential tripping hazards independently. Previous assistive technologies for the visually impaired often struggle in real-world scenarios due to the need for constant training and lack of robustness, which limits their effectiveness, especially in dynamic and unfamiliar environments, where accurate and efficient perception is crucial. Therefore, we frame our research question in this paper as: How can we assist pBLV in recognizing scenes, identifying objects, and detecting potential tripping hazards in unfamiliar environments, where existing assistive technologies often falter due to their lack of robustness? We hypothesize that by leveraging large pretrained foundation models and prompt engineering, we can create a system that effectively addresses the challenges faced by pBLV in unfamiliar environments. Motivated by the prevalence of large pretrained foundation models, particularly in assistive robotics applications, due to their accurate perception and robust contextual understanding in real-world scenarios induced by extensive pretraining, we present a pioneering approach that leverages foundation models to enhance visual perception for pBLV, offering detailed and comprehensive descriptions of the surrounding environment and providing warnings about potential risks. Specifically, our method begins by leveraging a large-image tagging model (i.e., Recognize Anything Model (RAM)) to identify all common objects present in the captured images. The recognition results and user query are then integrated into a prompt, tailored specifically for pBLV, using prompt engineering. By combining the prompt and input image, a vision-language foundation model (i.e., InstructBLIP) generates detailed and comprehensive descriptions of the environment and identifies potential risks in the environment by analyzing environmental objects and scenic landmarks, relevant to the prompt. We evaluate our approach through experiments conducted on both indoor and outdoor datasets. Our results demonstrate that our method can recognize objects accurately and provide insightful descriptions and analysis of the environment for pBLV.
PMCID:11122237
PMID: 38786557
ISSN: 2313-433x
CID: 5655102

Organoids to Remodel SARS-CoV-2 Research: Updates, Limitations and Perspectives

An, Yucheng; He, Yanjie; Ge, Nan; Guo, Jintao; Yang, Fan; Sun, Siyu
The novel COVID-19 pneumonia caused by the SARS-CoV-2 virus poses a significant threat to human health. Scientists have made significant efforts to control this virus, consequently leading to the development of novel research methods. Traditional animal and 2D cell line models might not be suitable for large-scale applications in SARS-CoV-2 research owing to their limitations. As an emerging modelling method, organoids have been applied in the study of various diseases. Their advantages include their ability to closely mirror human physiology, ease of cultivation, low cost, and high reliability; thus, they are considered to be a suitable choice to further the research on SARS-CoV-2. During the course of various studies, SARS-CoV-2 was shown to infect a variety of organoid models, exhibiting changes similar to those observed in humans. This review summarises the various organoid models used in SARS-CoV-2 research, revealing the molecular mechanisms of viral infection and exploring the drug screening tests and vaccine research that have relied on organoid models, hence illustrating the role of organoids in remodelling SARS-CoV-2 research.
PMID: 37196111
ISSN: 2152-5250
CID: 5544272

A new polymodal gating model of the proton-activated chloride channel

Zhao, Piao; Tang, Cheng; Yang, Yuqin; Xiao, Zhen; Perez-Miller, Samantha; Zhang, Heng; Luo, Guoqing; Liu, Hao; Li, Yaqi; Liao, Qingyi; Yang, Fan; Dong, Hao; Khanna, Rajesh; Liu, Zhonghua
The proton-activated chloride (PAC) channel plays critical roles in ischemic neuron death, but its activation mechanisms remain elusive. Here, we investigated the gating of PAC channels using its novel bifunctional modulator C77304. C77304 acted as a weak activator of the PAC channel, causing moderate activation by acting on its proton gating. However, at higher concentrations, C77304 acted as a weak inhibitor, suppressing channel activity. This dual function was achieved by interacting with 2 modulatory sites of the channel, each with different affinities and dependencies on the channel's state. Moreover, we discovered a protonation-independent voltage activation of the PAC channel that appears to operate through an ion-flux gating mechanism. Through scanning-mutagenesis and molecular dynamics simulation, we confirmed that E181, E257, and E261 in the human PAC channel serve as primary proton sensors, as their alanine mutations eliminated the channel's proton gating while sparing the voltage-dependent gating. This proton-sensing mechanism was conserved among orthologous PAC channels from different species. Collectively, our data unveils the polymodal gating and proton-sensing mechanisms in the PAC channel that may inspire potential drug development.
PMCID:10529583
PMID: 37713449
ISSN: 1545-7885
CID: 5594392

Exploring KRAS-mutant pancreatic ductal adenocarcinoma: a model validation study

Yang, Fan; He, Yanjie; Ge, Nan; Guo, Jintao; Yang, Fei; Sun, Siyu
INTRODUCTION/UNASSIGNED:Pancreatic ductal adenocarcinoma (PDAC) has the highest mortality rate among all solid tumors. Tumorigenesis is promoted by the oncogene KRAS, and KRAS mutations are prevalent in patients with PDAC. Therefore, a comprehensive understanding of the interactions between KRAS mutations and PDAC may expediate the development of therapeutic strategies for reversing the progression of malignant tumors. Our study aims at establishing and validating a prediction model of KRAS mutations in patients with PDAC based on survival analysis and mRNA expression. METHODS/UNASSIGNED:A total of 184 and 412 patients with PDAC from The Cancer Genome Atlas (TCGA) database and the International Cancer Genome Consortium (ICGC), respectively, were included in the study. RESULTS/UNASSIGNED:After tumor mutation profile and copy number variation (CNV) analyses, we established and validated a prediction model of KRAS mutations, based on survival analysis and mRNA expression, that contained seven genes: CSTF2, FAF2, KIF20B, AKR1A1, APOM, KRT6C, and CD70. We confirmed that the model has a good predictive ability for the prognosis of overall survival (OS) in patients with KRAS-mutated PDAC. Then, we analyzed differential biological pathways, especially the ferroptosis pathway, through principal component analysis, pathway enrichment analysis, Gene Ontology (GO) enrichment analysis, and gene set enrichment analysis (GSEA), with which patients were classified into low- or high-risk groups. Pathway enrichment results revealed enrichment in the cytokine-cytokine receptor interaction, metabolism of xenobiotics by cytochrome P450, and viral protein interaction with cytokine and cytokine receptor pathways. Most of the enriched pathways are metabolic pathways predominantly enriched by downregulated genes, suggesting numerous downregulated metabolic pathways in the high-risk group. Subsequent tumor immune infiltration analysis indicated that neutrophil infiltration, resting CD4 memory T cells, and resting natural killer (NK) cells correlated with the risk score. After verifying that the seven gene expression levels in different KRAS-mutated pancreatic cancer cell lines were similar to that in the model, we screened potential drugs related to the risk score. DISCUSSION/UNASSIGNED:This study established, analyzed, and validated a model for predicting the prognosis of PDAC based on risk stratification according to KRAS mutations, and identified differential pathways and highly effective drugs.
PMCID:10805828
PMID: 38268915
ISSN: 1664-3224
CID: 5625112

A new polymodal gating model of the proton-activated chloride channel

Zhao, Piao; Tang, Cheng; Yang, Yuqin; Xiao, Zhen; Perez-Miller, Samantha; Zhang, Heng; Luo, Guoqing; Liu, Hao; Li, Yaqi; Liao, Qingyi; Yang, Fan; Dong, Hao; Khanna, Rajesh; Liu, Zhonghua
ISI:001069360400003
ISSN: 1544-9173
CID: 5570002

A Potential Pitfall in POCUS of the Gallbladder: Beware of the Duodenum [Case Report]

Yang, Fan J; Kohen, Brian; Sanapala, Sowmya; Halperin, Michael
It is estimated that 20 million people in the United States have gallbladder disease. Of the patients who present to the Emergency Department (ED) with abdominal pain, 3-10% have acute cholecystitis. Point-of-care ultrasound (POCUS) evaluation of the biliary system is a valuable tool to diagnose gallbladder disease and can greatly expedite the diagnostic evaluation of patients. One source of error in POCUS of the gallbladder is imaging nearby structures that can mimic the gallbladder, such as the duodenum.
PMCID:9983711
PMID: 36896391
ISSN: 2369-8543
CID: 5455262

Inflammatory markers are poorly predictive of clinical outcomes among hospitalized patients with COVID-19

Barrett, Brendan; Pamphile, Styve; Yang, Fan; Naeem, Farnia; Kim, Jinsung; Annam, Jayabhargav; Borczuk, Rachel; Yellin, Shira; Bass, Carly; Fowler, Sabrina; Mosheyev, Maykl; Mayer, Yael Jessica; Friedman, Benjamin W
BACKGROUND:Inflammatory markers are often elevated in patients with COVID-19. The objective of this study is to assess the prognostic capability of these tests in predicting clinical outcomes. METHODS:and statistical significance for each correlation with outcomes. We also report positive predictive value, negative predictive value, sensitivity, specificity, positive likelihood ratios, and negative likelihood ratios. RESULTS:The mean age of our patient population was 62 (SD 16). Thirty-seven percent of patients self-reported Spanish/Hispanic/Latino ethnicity, 47% reported their race as Black or African-American, and 10% reported their race as non-Hispanic white. Inter-rater reliability was 96%. There was no laboratory value that had both sensitivity and specificity of at least 0.90, or that had a positive predictive value and negative predictive value of at least 0.90, or that had likelihood ratios that could reliably predict a severe course of disease. CONCLUSION/CONCLUSIONS:Inflammatory markers drawn within 48 h of arrival, though often correlated with clinical outcomes, are not individually highly predictive of which patients in a predominantly older and minority population will die or require intubation, RRT, or ICU admission.
PMCID:7685065
PMID: 33280969
ISSN: 1532-8171
CID: 5455282

Small bowel obstruction caused by massive fibroids [Case Report]

Sas, Daniel; Yang, Fan Jim; Agbayani, Nestor; Li, Siu Fai
A 44-year-old woman presented to the Emergency Department with abdominal pain. She had a history of fibroids and no prior surgeries. Ultrasonography and CT imaging revealed a small bowel obstruction and massive uterine fibroids. The patient required laparotomy to relieve the intestinal obstruction after conservative therapy failed. Massive uterine fibroids is a rare cause of small bowel obstruction which requires the vigilance of Emergency Medicine physicians.
PMID: 33041139
ISSN: 1532-8171
CID: 5455302

A clinicopathologic examination of myxofibrosarcoma. Do surgical margins significantly affect local recurrence rates in this infiltrative sarcoma subtype?

Dadrass, Farnaz; Gusho, Charles; Yang, Fan; Culvern, Chris; Bloom, Julie; Fillingham, Yale; Colman, Matthew; Gitelis, Steven; Blank, Alan
BACKGROUND AND OBJECTIVES/OBJECTIVE:Myxofibrosarcoma (MFS) is an aggressive soft tissue tumor with an unpredictable recurrence pattern. We sought to (a) determine whether margin status in MFS is correlated to rates of local recurrence (LR) and (b) identify demographic and treatment variables associated with disease-related outcomes in this population. METHODS:This retrospective study identified 42 surgically treated patients with MFS over 10 years at a single institution. Patient demographics, tumor characteristics, intraoperative variables, and disease-related outcomes were recorded. RESULTS:Thirty-three (83%) patients had negative surgical margins and seven (18%) had positive margins. Four of 32 patients (13%) with negative margins developed subsequent LR compared to six of seven (86%) patients with positive margins (p < .001). Three patients (75%) with metastatic disease were deceased at the end of the study, while five (15%) without metastasis were deceased (p = .024). CONCLUSIONS:Positive margin procedures for MFS were associated with LR. However, negative surgical margins demonstrated a relatively high rate of LR compared to other soft tissue sarcoma subtypes. Furthermore, though MFS tends to locally recur and have a propensity for distant metastasis, patients are observed to have a higher probability of death from other causes.
PMID: 33125727
ISSN: 1096-9098
CID: 5455272