Predicting dementia diagnosis from cognitive footprints in electronic health records: a case-control study protocol

INTRODUCTION: Dementia is a group of disabling disorders that can be devastating for persons living with it and for their families. Data-informed decision-making strategies to identify individuals at high risk of dementia are essential to facilitate large-scale prevention and early intervention. This population-based case-control study aims to develop and validate a clinical algorithm for predicting dementia diagnosis, based on the cognitive footprint in personal and medical history. METHODS AND ANALYSIS: We will use territory-wide electronic health records from the Clinical Data Analysis and Reporting System (CDARS) in Hong Kong between 1 January 2001 and 31 December 2018. All individuals who were at least 65 years old by the end of 2018 will be identified from CDARS. A random sample of control individuals who did not receive any diagnosis of dementia will be matched with those who did receive such a diagnosis by age, gender and index date with 1:1 ratio. Exposure to potential protective/risk factors will be included in both conventional logistic regression and machine-learning models. Established risk factors of interest will include diabetes mellitus, midlife hypertension, midlife obesity, depression, head injuries and low education. Exploratory risk factors will include vascular disease, infectious disease and medication. The prediction accuracy of several state-of-the-art machine-learning algorithms will be compared. ETHICS AND DISSEMINATION: This study was approved by Institutional Review Board of The University of Hong Kong/Hospital Authority Hong Kong West Cluster (UW 18-225). Patients' records are anonymised to protect privacy. Study results will be disseminated through peer-reviewed publications. Codes of the resulted dementia risk prediction algorithm will be made publicly available at the website of the Tools to Inform Policy: Chinese Communities' Action in Response to Dementia project (https://www.tip-card.hku.hk/)

A thermostable leucine aminopeptidase from Bacillus kaustophilus CCRC 11223

Two degenerate primers established from the consensus sequences of bacterial leucine aminopeptidases (LAP) were used to amplify a 360-bp gene fragment from the chromosomal DNA of thermophilic Bacillus kaustophilus CCRC 11223 and the amplified fragment was successfully used as a probe to clone a leucine aminopeptidase (lap) gene from a genomic library of the strain. The gene consists of an open reading frame (ORF) of 1,494 bp and encodes a protein of 497 amino acid residues with a calculated molecular mass of 53.7 kDa. The complete amino acid sequence of the cloned enzyme showed greater than 30% identity with prokaryotic and eukaryotic LAPs. Phylogenetic analysis showed that B. kaustophilus LAP is closely related to the enzyme from Bacillus subtilis and is grouped with the M17 family. His(6)-tagged LAP was generated in Escherichia coli by cloning the coding region into pQE-30 and the recombinant enzyme was purified by nickel-chelate chromatography. The pH and temperature optima for the purified enzyme were 8 and 65degreesC, respectively, and 50% of its activity remained after incubation at 60degreesC for 32 min. The enzyme preferentially hydrolyzed L-leucine-p-nitroanilide (L-Leu-p-NA) followed by Cys derivative

Identifying dementia from cognitive footprints in hospital records among Chinese older adults: a machine-learning study

Background: By combining theory-driven and data-driven methods, this study aimed to develop dementia predictive algorithms among Chinese older adults guided by the cognitive footprint theory. Methods: Electronic medical records from the Clinical Data Analysis and Reporting System in Hong Kong were employed. We included patients with dementia diagnosed at 65+ between 2010 and 2018, and 1:1 matched dementia-free controls. We identified 51 features, comprising exposures to established modifiable factors and other factors before and after 65 years old. The performances of four machine learning models, including LASSO, Multilayer perceptron (MLP), XGBoost, and LightGBM, were compared with logistic regression models, for all patients and subgroups by age. Findings: A total of 159,920 individuals (40.5% male; mean age [SD]: 83.97 [7.38]) were included. Compared with the model included established modifiable factors only (area under the curve [AUC] 0.689, 95% CI [0.684, 0.694]), the predictive accuracy substantially improved for models with all factors (0.774, [0.770, 0.778]). Machine learning and logistic regression models performed similarly, with AUC ranged between 0.773 (0.768, 0.777) for LASSO and 0.780 (0.776, 0.784) for MLP. Antipsychotics, education, antidepressants, head injury, and stroke were identified as the most important predictors in the total sample. Age-specific models identified different important features, with cardiovascular and infectious diseases becoming prominent in older ages. Interpretation: The models showed satisfactory performances in identifying dementia. These algorithms can be used in clinical practice to assist decision making and allow timely interventions cost-effectively. Funding: The Research Grants Council of Hong Kong under the Early Career Scheme 27110519

Observation of Two New N* Peaks in J/psi -> ppi−nˉp pi^- \bar n and pˉπ+n\bar p\pi^+n Decays

The $\pi N$ system in decays of $J/\psi\to\bar NN\pi$ is limited to be isospin 1/2 by isospin conservation. This provides a big advantage in studying $N^*\to \pi N$ compared with $\pi N$ and $\gamma N$ experiments which mix isospin 1/2 and 3/2 for the $\pi N$ system. Using 58 million $J/\psi$ decays collected with the Beijing Electron Positron Collider, more than 100 thousand $J/\psi \to p \pi^- \bar n + c.c.$ events are obtained. Besides two well known $N^*$ peaks at 1500 MeV and 1670 MeV, there are two new, clear $N^*$ peaks in the $p\pi$ invariant mass spectrum around 1360 MeV and 2030 MeV. They are the first direct observation of the $N^*(1440)$ peak and a long-sought "missing" $N^*$ peak above 2 GeV in the $\pi N$ invariant mass spectrum. A simple Breit-Wigner fit gives the mass and width for the $N^*(1440)$ peak as $1358\pm 6 \pm 16$ MeV and $179\pm 26\pm 50$ MeV, and for the new $N^*$ peak above 2 GeV as $2068\pm 3^{+15}_{-40}$ MeV and $165\pm 14\pm 40$ MeV, respectively

Experimental NMR Realization of A Generalized Quantum Search Algorithm

A generalized quantum search algorithm, where phase inversions for the marked state and the prepared state are replaced by $\pi/2$ phase rotations, is realized in a 2-qubit NMR heteronuclear system. The quantum algorithm searches a marked state with a smaller step compared to standard Grover algorithm. Phase matching requirement in quantum searching is demonstrated by comparing it with another generalized algorithm where the two phase rotations are $\pi/2$ and $3\pi/2$ respectively. Pulse sequences which include non 90 degree pulses are given.Comment: 12 pages, 2 figures, accepted for publication in Plysics Letters

Direct Measurements of the Branching Fractions for D0→K−e+νeD^0 \to K^-e^+\nu_e and D0→π−e+νeD^0 \to \pi^-e^+\nu_e and Determinations of the Form Factors f+K(0)f_{+}^{K}(0) and f+π(0)f^{\pi}_{+}(0)

The absolute branching fractions for the decays $D^0 \to K^-e ^+\nu_e$ and $D^0 \to \pi^-e^+\nu_e$ are determined using $7584\pm 198 \pm 341$ singly tagged $\bar D^0$ sample from the data collected around 3.773 GeV with the BES-II detector at the BEPC. In the system recoiling against the singly tagged $\bar D^0$ meson, $104.0\pm 10.9$ events for $D^0 \to K^-e ^+\nu_e$ and $9.0 \pm 3.6$ events for $D^0 \to \pi^-e^+\nu_e$ decays are observed. Those yield the absolute branching fractions to be $BF(D^0 \to K^-e^+\nu_e)=(3.82 \pm 0.40\pm 0.27)$ and $BF(D^0 \to \pi^-e^+\nu_e)=(0.33 \pm 0.13\pm 0.03)$. The vector form factors are determined to be $|f^K_+(0)| = 0.78 \pm 0.04 \pm 0.03$ and $|f^{\pi}_+(0)| = 0.73 \pm 0.14 \pm 0.06$. The ratio of the two form factors is measured to be $|f^{\pi}_+(0)/f^K_+(0)|= 0.93 \pm 0.19 \pm 0.07$.Comment: 6 pages, 5 figure

Observation of the decay \psip\rar\kstark

Using 14 million $\psi(2S)$ events collected with the BESII detector, branching fractions of \psip\rar\kstarkpm and \kstarknn are determined to be: \calB(\psip\rar\kstarkpm)=(2.9^{+1.3}_{-1.7}\pm0.4)\times 10^{-5} and \calB(\psip\rar\kstarknn)=(13.3^{+2.4}_{-2.7}\pm1.9)\times 10^{-5}. The results confirm the violation of the "12%" rule for these two decay channels with higher precision. A large isospin violation between the charged and neutral modes is observed.Comment: 5 pages, 3 figure

Measurements of J/psi Decays into 2(pi+pi-)eta and 3(pi+pi-)eta

Based on a sample of 5.8X 10^7 J/psi events taken with the BESII detector, the branching fractions of J/psi--> 2(pi+pi-)eta and J/psi-->3(pi+pi-)eta are measured for the first time to be (2.26+-0.08+-0.27)X10^{-3} and (7.24+-0.96+-1.11)X10^{-4}, respectively.Comment: 11 pages, 6 figure

BESII Detector Simulation

A Monte Carlo program based on Geant3 has been developed for BESII detector simulation. The organization of the program is outlined, and the digitization procedure for simulating the response of various sub-detectors is described. Comparisons with data show that the performance of the program is generally satisfactory.Comment: 17 pages, 14 figures, uses elsart.cls, to be submitted to NIM

Measurement of branching fractions for the inclusive Cabibbo-favored ~K*0(892) and Cabibbo-suppressed K*0(892) decays of neutral and charged D mesons

The branching fractions for the inclusive Cabibbo-favored ~K*0 and Cabibbo-suppressed K*0 decays of D mesons are measured based on a data sample of 33 pb-1 collected at and around the center-of-mass energy of 3.773 GeV with the BES-II detector at the BEPC collider. The branching fractions for the decays D+(0) -> ~K*0(892)X and D0 -> K*0(892)X are determined to be BF(D0 -> \~K*0X) = (8.7 +/- 4.0 +/- 1.2)%, BF(D+ -> ~K*0X) = (23.2 +/- 4.5 +/- 3.0)% and BF(D0 -> K*0X) = (2.8 +/- 1.2 +/- 0.4)%. An upper limit on the branching fraction at 90% C.L. for the decay D+ -> K*0(892)X is set to be BF(D+ -> K*0X) < 6.6%