Psychoactive Plant Database - Neuroactive Phytochemical Collection

1. Sci Rep. 2024 Nov 6;14(1):26939. doi: 10.1038/s41598-024-77740-9.

Automated image acquisition and analysis of graphene and hexagonal boron nitride 
from pristine to highly defective and amorphous structures.

Propst D(1)(2), Joudi W(1)(2), Längle M(1), Madsen J(1), Kofler C(1)(2), Mayer 
BM(1), Lamprecht D(3), Mangler C(1), Filipovic L(3), Susi T(4), Kotakoski J(5).

Author information:
(1)Faculty of Physics, University of Vienna, Boltzmanngasse 5, 1090, Vienna, 
Austria.
(2)Vienna Doctoral School in Physics, University of Vienna, Boltzmanngasse 5, 
1090, Vienna, Austria.
(3)Institute for Microelectronics, TU Wien, Gusshausstrasse 27-29/E360, 1040, 
Vienna, Austria.
(4)Faculty of Physics, University of Vienna, Boltzmanngasse 5, 1090, Vienna, 
Austria. toma.susi@univie.ac.at.
(5)Faculty of Physics, University of Vienna, Boltzmanngasse 5, 1090, Vienna, 
Austria. jani.kotakoski@univie.ac.at.

Defect-engineered and even amorphous two-dimensional (2D) materials have 
recently gained interest due to properties that differ from their pristine 
counterparts. Since these properties are highly sensitive to the exact atomic 
structure, it is crucial to be able to characterize them at atomic resolution 
over large areas. This is only possible when the imaging process is automated to 
reduce the time spent on manual imaging, which at the same time reduces the 
observer bias in selecting the imaged areas. Since the necessary datasets 
include at least hundreds if not thousands of images, the analysis process 
similarly needs to be automated. Here, we introduce disorder into graphene and 
monolayer hexagonal boron nitride (hBN) using low-energy argon ion irradiation, 
and characterize the resulting disordered structures using automated scanning 
transmission electron microscopy annular dark field imaging combined with 
convolutional neural network-based analysis techniques. We show that disorder 
manifests in these materials in a markedly different way, where graphene 
accommodates vacancy-type defects by transforming hexagonal carbon rings into 
other polygonal shapes, whereas in hBN the disorder is observed simply as vacant 
lattice sites with very little rearrangement of the remaining atoms. 
Correspondingly, in the case of graphene, the highest introduced disorder leads 
to an amorphous membrane, whereas in hBN, the highly defective lattice contains 
a large number of vacancies and small pores with no indication of amorphisation. 
Overall, our study demonstrates that combining automated imaging and image 
analysis is a powerful way to characterize the structure of disordered and 
amorphous 2D materials, while also illustrating some of the remaining 
shortcomings with this methodology.

© 2024. The Author(s).

DOI: 10.1038/s41598-024-77740-9
PMID: 39506053


2. Sci Rep. 2024 Nov 6;14(1):26895. doi: 10.1038/s41598-024-74730-9.

Algorithms for the identification of prevalent diabetes in the All of Us 
Research Program validated using polygenic scores.

Szczerbinski L(#)(1)(2)(3)(4)(5), Mandla R(#)(3)(4)(5)(6), Schroeder 
P(#)(3)(4)(5), Porneala BC(7), Li JH(3)(4)(5)(8), Florez JC(3)(4)(5)(8), 
Mercader JM(9)(10)(11)(12), Udler MS(13)(14)(15)(16), Manning 
AK(17)(18)(19)(20).

Author information:
(1)Department of Endocrinology, Diabetology and Internal Medicine, Medical 
University of Bialystok, 15-276, Bialystok, Poland.
(2)Clinical Research Centre, Medical University of Bialystok, 15-276, Bialystok, 
Poland.
(3)Programs in Metabolism and Medical & Population Genetics, Broad Institute of 
Harvard and MIT, 415 Main St., Cambridge, MA, 02142, USA.
(4)Center for Genomic Medicine, Massachusetts General Hospital, Boston, USA.
(5)Diabetes Unit, Department of Medicine, Massachusetts General Hospital, 
Boston, USA.
(6)Cardiology Division, Department of Medicine and Cardiovascular Research 
Institute, University of California, San Francisco, USA.
(7)Division of General Internal Medicine, Department of Medicine, Massachusetts 
General Hospital, Boston, USA.
(8)Department of Medicine, Harvard Medical School, Boston, MA, USA.
(9)Programs in Metabolism and Medical & Population Genetics, Broad Institute of 
Harvard and MIT, 415 Main St., Cambridge, MA, 02142, USA. 
mercader@broadinstitute.org.
(10)Center for Genomic Medicine, Massachusetts General Hospital, Boston, USA. 
mercader@broadinstitute.org.
(11)Diabetes Unit, Department of Medicine, Massachusetts General Hospital, 
Boston, USA. mercader@broadinstitute.org.
(12)Department of Medicine, Harvard Medical School, Boston, MA, USA. 
mercader@broadinstitute.org.
(13)Programs in Metabolism and Medical & Population Genetics, Broad Institute of 
Harvard and MIT, 415 Main St., Cambridge, MA, 02142, USA. 
MUDLER@mgh.harvard.edu.
(14)Center for Genomic Medicine, Massachusetts General Hospital, Boston, USA. 
MUDLER@mgh.harvard.edu.
(15)Diabetes Unit, Department of Medicine, Massachusetts General Hospital, 
Boston, USA. MUDLER@mgh.harvard.edu.
(16)Department of Medicine, Harvard Medical School, Boston, MA, USA. 
MUDLER@mgh.harvard.edu.
(17)Programs in Metabolism and Medical & Population Genetics, Broad Institute of 
Harvard and MIT, 415 Main St., Cambridge, MA, 02142, USA. 
amanning@broadinstitute.org.
(18)Center for Genomic Medicine, Massachusetts General Hospital, Boston, USA. 
amanning@broadinstitute.org.
(19)Department of Medicine, Harvard Medical School, Boston, MA, USA. 
amanning@broadinstitute.org.
(20)Clinical and Translational Epidemiology Unit, Department of Medicine, 
Massachusetts General Hospital, Boston, USA. amanning@broadinstitute.org.
(#)Contributed equally

The All of Us Research Program (AoU) is an initiative designed to gather a 
comprehensive and diverse dataset from at least one million individuals across 
the USA. This longitudinal cohort study aims to advance research by providing a 
rich resource of genetic and phenotypic information, enabling powerful studies 
on the epidemiology and genetics of human diseases. One critical challenge to 
maximizing its use is the development of accurate algorithms that can 
efficiently and accurately identify well-defined disease and disease-free 
participants for case-control studies. This study aimed to develop and validate 
type 1 (T1D) and type 2 diabetes (T2D) algorithms in the AoU cohort, using 
electronic health record (EHR) and survey data. Building on existing algorithms 
and using diagnosis codes, medications, laboratory results, and survey data, we 
developed and implemented algorithms for identifying prevalent cases of type 1 
and type 2 diabetes. The first set of algorithms used only EHR data (EHR-only), 
and the second set used a combination of EHR and survey data (EHR+). A universal 
algorithm was also developed to identify individuals without diabetes. The 
performance of each algorithm was evaluated by testing its association with 
polygenic scores (PSs) for type 1 and type 2 diabetes. We demonstrated the 
feasibility and utility of using AoU EHR and survey data to employ diabetes 
algorithms. For T1D, the EHR-only algorithm showed a stronger association with 
T1D-PS compared to the EHR + algorithm (DeLong p-value = 3 × 10-5). For T2D, the 
EHR + algorithm outperformed both the EHR-only and the existing T2D definition 
provided in the AoU Phenotyping Library (DeLong p-values = 0.03 and 1 × 10-4, 
respectively), identifying 25.79% and 22.57% more cases, respectively, and 
providing an improved association with T2D PS. We provide a new validated type 1 
diabetes definition and an improved type 2 diabetes definition in AoU, which are 
freely available for diabetes research in the AoU. These algorithms ensure 
consistency of diabetes definitions in the cohort, facilitating high-quality 
diabetes research.

© 2024. The Author(s).

DOI: 10.1038/s41598-024-74730-9
PMID: 39505999 [Indexed for MEDLINE]


3. Nan Fang Yi Ke Da Xue Xue Bao. 2024 Sep 20;44(9):1776-1782. doi: 
10.12122/j.issn.1673-4254.2024.09.18.

[High expression of CREM is associated with poor prognosis in gastric cancer 
patients].

[Article in Chinese]

Ye M(1)(2), Wu H(2), Mei Y(2), Zhang Q(2).

Author information:
(1)School of Medicine, South China University of Technology, Guangzhou 510006, 
China.
(2)Department of Pathology, Guangdong Provincial People's Hospital (Guangdong 
Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, 
China.

OBJECTIVE: To analyze the expression of CREM in gastric cancer (GC) and its 
correlation with prognosis of the patients.
METHODS: TCGA and GEO databases were used to analyze the expression levels of 
CREM mRNA in GC and adjacent tissues. Immunohistochemistry was used to examine 
the expression of CREM protein in 43 pairs of GC and adjacent tissues, and the 
correlation of CREM expression with clinicopathological features of the patients 
was analyzed. Kaplan-Meier survival analysis was used to explore the 
relationship between CREM expression and survival of GC patients. LinkedOmics 
database was used to annotate the GO function and KEGG pathway enrichment of 
CREM-related genes.
RESULTS: Database analysis showed that CREM was highly expressed in GC tissues 
(P < 0.05) and positively correlated with poor prognosis in GC patients 
(P=0.01). Immunohistochemistry results showed significantly higher CREM 
expression in GC tissues than in paired adjacent tissues (P < 0.0001), and its 
expression level was correlated with T-stage and N-stage of the tumor (P < 
0.05). The overall survival of GC patients with high expression of CREM was 
shorter (RR=4.02, P=0.0046). Gene enrichment analysis showed that high CREM 
expression promotes occurrence and progression of GC very likely through the 
cell adhesion signaling pathway.
CONCLUSION: CREM is highly expressed in GC, and its high expression is 
associated with a poor prognosis of GC patients, suggesting the potential of 
CREM to serve as a prognostic indicator for GC.

DOI: 10.12122/j.issn.1673-4254.2024.09.18
PMID: 39505346 [Indexed for MEDLINE]


4. J Colloid Interface Sci. 2024 Nov 2;680(Pt A):202-214. doi: 
10.1016/j.jcis.2024.11.003. Online ahead of print.

Nuclear-targeted smart nanoplatforms featuring double-shell hollow mesoporous 
copper sulfide coated with manganese dioxide synergistically potentiate 
chemotherapy and immunotherapy in hepatocellular carcinoma cells.

Li LS(1), Chen PW(1), Zhao XJ(1), Cheng D(1), Liu BB(1), Tang XJ(1), Zhu WQ(1), 
Yang X(1), Zhao MX(2).

Author information:
(1)Henan Key Laboratory of Natural Medicine Innovation and Transformation, Henan 
University, Kaifeng 475004, China; State Key Laboratory of Antiviral Drugs, 
Henan University, Kaifeng 475004, China.
(2)Henan Key Laboratory of Natural Medicine Innovation and Transformation, Henan 
University, Kaifeng 475004, China; State Key Laboratory of Antiviral Drugs, 
Henan University, Kaifeng 475004, China; The Zhongzhou Laboratory for 
Integrative Biology, Henan University, Kaifeng, China. Electronic address: 
zhaomeixia2011@henu.edu.cn.

Smart nanoplatforms designed for nuclear-targeted delivery of chemotherapeutic 
agents to tumor sites are pivotal in advancing tumor treatment and 
immunotherapy. Herein, we introduced a novel nuclear-targeting double-shell 
smart nanoplatform (HMCuS/Pt/ICG@MnO2@9R-P201 (HMCPIM9P)), which synergistically 
enhances chemotherapy, photodynamic therapy (PDT), photothermal therapy (PTT), 
immunotherapy and chemodynamic therapy (CDT). The core of this nanoplatform 
consists of double-shell multifunctional nanoparticles (HMCuS@MnO2) that enable 
targeted delivery of the photosensitizer Indocyanine Green (ICG) and the 
chemotherapeutic agent cisplatin (Pt). By effectively consuming glutathione 
(GSH), these nanoparticles boost the chemotherapeutic efficacy of Pt. 
Additionally, the manganese ion (Mn2+) present activate the cyclic GMP-AMP 
synthase (cGAS)-stimulator of interferon genes (STING) (cGAS-STING) pathway, 
bolstering adaptive immune responses against tumors and elevating the level of 
tumor-infiltrating CD8+ T cells. The incorporation of the hepatoma-targeting 
peptide (9R-P201 peptide) allows the system to exhibit FOXM1 receptor-mediated 
nuclear targeting properties specifically in hepatocellular carcinoma (HCC). 
Notably, when combined with near-infrared (NIR) light, HMCPIM9P demonstrated a 
remarkable tumor inhibition rate of 95.6 %, fostered a robust immune response, 
and significantly inhibited tumor growth and recurrence. Overall, the smart 
nanoplatform boasts active nuclear targeting capabilities, enabling the 
enrichment of chemotherapeutic agents at tumor sites, and holds great potential 
for synergistic applications in enhancing chemotherapy and immunotherapy for 
HCC.

Copyright © 2024 Elsevier Inc. All rights reserved.

DOI: 10.1016/j.jcis.2024.11.003
PMID: 39504750

Conflict of interest statement: Declaration of competing interest The authors 
declare that they have no known competing financial interests or personal 
relationships that could have appeared to influence the work reported in this 
paper.


5. Am J Sports Med. 2024 Nov;52(13):3286-3294. doi: 10.1177/03635465241288227.

Surgical Predictors of Clinical Outcome 6 Years After Revision ACL 
Reconstruction.

MARS Group(1); Wright RW(2)(1), Huston LJ(2)(1), Haas AK(3)(1), Pennings 
JS(2)(1), Allen CR(4)(1), Cooper DE(5)(1), DeBerardino TM(6)(1), Dunn WR(7)(1), 
Lantz BBA(8)(1), Spindler KP(9)(1), Stuart MJ(10)(1), Amendola AN(11)(1), 
Annunziata CC(12)(1), Arciero RA(13)(1), Bach BR Jr(14)(1), Baker CL 3rd(15)(1), 
Bartolozzi AR(16)(1), Baumgarten KM(17)(1), Berg JH(18)(1), Bernas GA(19)(1), 
Brockmeier SF(20)(1), Brophy RH(3)(1), Bush-Joseph CA(14)(1), Butler JB 
5th(21)(1), Carey JL(22)(1), Carpenter JE(23)(1), Cole BJ(14)(1), Cooper 
JM(24)(1), Cox CL(2)(1), Creighton RA(25)(1), David TS(26)(1), Flanigan 
DC(27)(1), Frederick RW(28)(1), Ganley TJ(29)(1), Gatt CJ Jr(30)(1), Gecha 
SR(31)(1), Giffin JR(32)(1), Hame SL(33)(1), Hannafin JA(34)(1), Harner 
CD(35)(1), Harris NL Jr(36)(1), Hechtman KS(37)(1), Hershman EB(38)(1), 
Hoellrich RG(8)(1), Johnson DC(39)(1), Johnson TS(39)(1), Jones MH(40)(1), 
Kaeding CC(27)(1), Kamath GV(25)(1), Klootwyk TE(41)(1), Levy BA(42)(1), Ma 
CB(43)(1), Maiers GP 2nd(41)(1), Marx RG(34)(1), Matava MJ(3)(1), Mathien 
GM(44)(1), McAllister DR(33)(1), McCarty EC(45)(1), McCormack RG(46)(1), Miller 
BS(23)(1), Nissen CW(47)(1), O'Neill DF(48)(1), Owens BD(49)(1), Parker 
RD(9)(1), Purnell ML(50)(1), Ramappa AJ(51)(1), Rauh MA(19)(1), Rettig 
AC(41)(1), Sekiya JK(23)(1), Shea KG(52)(1), Sherman OH(53)(1), Slauterbeck 
JR(54)(1), Smith MV(3)(1), Spang JT(25)(1), Svoboda SJ(55)(1), Taft TN(25)(1), 
Tenuta JJ(56)(1), Tingstad EM(57)(1), Vidal AF(58)(1), Viskontas DG(59)(1), 
White RA(60)(1), Williams JS Jr(61)(1), Wolcott ML(62)(1), Wolf BR(63)(1), York 
JJ(64)(1).

Author information:
(1)Investigation performed at Vanderbilt University, Nashville, Tennessee, USA.
(2)Vanderbilt University, Nashville, TN, USA.
(3)Washington University in St Louis, St Louis, MO, USA.
(4)Yale University, New Haven, CT, USA.
(5)W.B. Carrell Memorial Clinic, Dallas, TX, USA.
(6)UT Health, San Antonio, TX, USA.
(7)Fondren Orthopedic Group, Houston, TX, USA.
(8)Slocum Research and Education Foundation, Eugene, OR, USA.
(9)Cleveland Clinic, Cleveland, OH, USA.
(10)Mayo Clinic, Rochester, MN, USA.
(11)U.S. Department of Agriculture, Agricultural Research Service. 1992-2016. Dr. Duke's Phytochemical and Ethnobotanical Databases. Home Page, http://phytochem.nal.usda.gov/ http://dx.doi.org/10.15482/USDA.ADC/1239279 University, Durham, NC, USA.
(12)Commonwealth Orthopaedics & Rehabilitation, Arlington, VA, USA.
(13)University of Connecticut Health Center, Farmington, CT, USA.
(14)Rush University Medical Center, Chicago, IL, USA.
(15)The Hughston Clinic, Columbus, GA, USA.
(16)3B Orthopaedics, University of Pennsylvania Health System, Philadelphia, PA, 
USA.
(17)Orthopedic Institute, Sioux Falls, SD, USA.
(18)Town Center Orthopaedic Associates, Reston, VA, USA.
(19)State University of New York at Buffalo, Buffalo, NY, USA.
(20)University of Virginia, Charlottesville, VA, USA.
(21)Orthopedic and Fracture Clinic, Portland, OR, USA.
(22)University of Pennsylvania, Philadelphia, PA, USA.
(23)University of Michigan, Ann Arbor, MI, USA.
(24)HealthPartners Specialty Center, St Paul, MN, USA.
(25)University of North Carolina Medical Center, Chapel Hill, NC, USA.
(26)Synergy Specialists Medical Group, San Diego, CA, USA.
(27)The Ohio State University, Columbus, OH, USA.
(28)The Rothman Institute/Thomas Jefferson University, Philadelphia, PA, USA.
(29)Children's Hospital of Philadelphia, Philadelphia, PA, USA.
(30)University Orthopaedic Associates LLC, Princeton, NJ, USA.
(31)Princeton Orthopaedic Associates, Princeton, NJ, USA.
(32)Fowler Kennedy Sport Medicine Clinic, University of Western Ontario, London, 
ON, Canada.
(33)University of California Los Angeles, Los Angeles, CA, USA.
(34)Hospital for Special Surgery, New York, NY, USA.
(35)University of Texas Health Center, Houston, TX, USA.
(36)Grand River Health, Rifle, CO, USA.
(37)Miami Orthopedics and Sports Medicine Institute, Coral Gables, FL, USA.
(38)Lenox Hill Hospital, New York, NY, USA.
(39)National Sports Medicine Institute, Leesburg, VA, USA.
(40)Brigham and Women's Hospital, Boston, MA, USA.
(41)Forte Sports Medicine and Orthopedics, Indianapolis, IN, USA.
(42)Orlando Health, Orlando, FL, USA.
(43)University of California, San Francisco, CA, USA.
(44)Knoxville Orthopedic Clinic, Knoxville, TN, USA.
(45)University of Colorado Denver School of Medicine, Denver, CO, USA.
(46)University of British Columbia/Fraser Health Authority, Vancouver, BC, 
Canada.
(47)Connecticut Children's Medical Center, Hartford, CT, USA.
(48)The Alpine Clinic, Plymouth, NH, USA.
(49)Warren Alpert Medical School, Brown University, Providence, RI, USA.
(50)Valley Ortho, Aspen, CO, USA.
(51)Beth Israel Deaconess Medical Center, Boston, MA, USA.
(52)Stanford University, Palo Alto, CA, USA.
(53)NYU Hospital for Joint Diseases, New York, NY, USA.
(54)UNC Health Southeastern, Lumberton, NC, USA.
(55)MedStar Orthopaedic and Sports Center, Washington, DC, USA.
(56)Albany Medical Center, Albany, NY, USA.
(57)Inland Orthopaedic Surgery and Sports Medicine Clinic, Pullman, WA, USA.
(58)The Steadman Clinic, Vail, CO, USA.
(59)Fraser Orthopaedic Institute, New Westminster, Vancouver, BC, Canada.
(60)Fitzgibbon's Hospital, Marshall, MO, USA.
(61)Cleveland Clinic, Euclid, OH, USA.
(62)University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
(63)University of Iowa Hospitals and Clinics, Iowa City, IA, USA.
(64)Luminis Health Orthopedics, Pasadena, MD, USA.

BACKGROUND: Revision anterior cruciate ligament (ACL) reconstruction has been 
documented to have inferior outcomes compared with primary ACL reconstruction. 
The reasons why remain unknown.
PURPOSE: To determine whether surgical factors performed at the time of revision 
ACL reconstruction can influence a patient's outcome at 6-year follow-up.
STUDY DESIGN: Cohort study; Level of evidence, 2.
METHODS: Patients who underwent revision ACL reconstruction were identified and 
prospectively enrolled between 2006 and 2011. Data collected included baseline 
patient characteristics, surgical technique and pathology, and a series of 
validated patient-reported outcome instruments: Knee injury and Osteoarthritis 
Outcome Score (KOOS), International Knee Documentation Committee (IKDC) 
subjective form, Western Ontario and McMaster Universities Osteoarthritis Index 
(WOMAC), and Marx activity rating score. Patients were followed up for 6 years 
and asked to complete the identical set of outcome instruments. Regression 
analysis was used to control for baseline patient characteristics and surgical 
variables to assess the surgical risk factors for clinical outcomes 6 years 
after surgery.
RESULTS: A total of 1234 patients were enrolled (716 men, 58%; median age, 26 
years), and 6-year follow-up was obtained on 79% of patients (980/1234). Using 
an interference screw for femoral fixation compared with a cross-pin resulted in 
significantly better outcomes in 6-year IKDC scores (odds ratio [OR], 2.2; 95% 
CI, 1.2-3.9; P = .008) and KOOS sports/recreation and quality of life subscale 
scores (OR range, 2.2-2.7; 95% CI, 1.2-4.8; P < .01). Use of an interference 
screw compared with a cross-pin resulted in a 2.6 times less likely chance of 
having a subsequent surgery within 6 years. Use of an interference screw for 
tibial fixation compared with any combination of tibial fixation techniques 
resulted in significantly improved scores for IKDC (OR, 1.96; 95% CI, 1.3-2.9; P 
= .001); KOOS pain, activities of daily living, and sports/recreation subscales 
(OR range, 1.5-1.6; 95% CI, 1.0-2.4; P < .05); and WOMAC pain and activities of 
daily living subscales (OR range, 1.5-1.8; 95% CI, 1.0-2.7; P < .05). Use of a 
transtibial surgical approach compared with an anteromedial portal approach 
resulted in significantly improved KOOS pain and quality of life subscale scores 
at 6 years (OR, 1.5; 95% CI, 1.02-2.2; P≤ .04).
CONCLUSION: There are surgical variables at the time of ACL revision that can 
modify clinical outcomes at 6 years. Opting for a transtibial surgical approach 
and choosing an interference screw for femoral and tibial fixation improved 
patients' odds of having a significantly better 6-year clinical outcome in this 
cohort.

DOI: 10.1177/03635465241288227
PMID: 39503722 [Indexed for MEDLINE]

Conflict of interest statement: One or more of the authors has declared the 
following potential conflict of interest or source of funding: This project was 
funded by grant No. 5R01-AR060846 from the National Institutes of 
Health/National Institute of Arthritis and Musculoskeletal and Skin Diseases. 
All author disclosures are listed in the Appendix (available in the online 
version of this article). AOSSM checks author disclosures against the Open 
Payments Database (OPD). AOSSM has not conducted an independent investigation on 
the OPD and disclaims any liability or responsibility relating thereto.